Biomarkers for Babesia

ABSTRACT

The present invention provides protein-based biomarkers and biomarker combinations that are useful in qualifying babesia status in a patient. In particular, the biomarkers of this invention are useful to classify a subject sample as infected with babesia or not infected with babesia. The biomarkers can be detected by SELDI mass spectrometry.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Ser.No. 60/749,449 filed Dec. 12, 2005, incorporated herein by reference inits entirety.

FIELD

The invention relates generally to clinical diagnostics.

BACKGROUND

Babesiosis, also referred to as babesia, is a disease caused byubiquitous protozoan parasites of the Babesia family. These parasitesand closely-related species have a worldwide distribution and infect awide range of mammals (Dao Compr Ther. 1996;22(11):713-8; Krause MedClin North Am. 2002;86(2):361-73). The vast majority of human cases inNorth America are caused by the rodent parasite B Babesia microti. Inother parts of the world, the cattle parasite B. divergens is implicatedmore often (Zintl et al. Clin Microbiol Rev. 2003;16(4):622-36). Theseparasites typically cause disease in relatively discreet areas relatedto the presence of the appropriate hard-bodied tick vectors (ixodidspecies). In North America, these ticks and humans ‘mingle’ primarily inthe recreational and residential areas of the Northeast (eg: Maine toNorth Carolina), around the Great Lakes (including discreet regions ofsouthern Ontario) and in more limited foci on the West Coast (Washington& California). Risk factors for infection include residence, recreationor work in one of these regions that results in exposure to tick habitat(Krause et al. J Clin Microbiol. 1991;29(l):1-4). In the USA, it hasbeen estimated that hundreds of cases occur annually (Herwalt StricklandGT (ed) Hunter's Tropical Medicine and Emerging InfectiousDiseases—English Edition. Saunders: Philadelphia 2000. Pp68890).Serosurveys conducted in Babesia-endemic regions suggest that infectionrates may be much (Hunfeld et al. J Clin Microbiol. 2002;40(7):2431-6;Krause et al. Am J Trop Med Hyg. 2003;68(4):431-6). Babesia species areall readily transmitted by blood transfusion (Smith et al. Clin Lab Sci.2003;16(4):239-45, 251, Dodd Int J Hematol. 2004;80(4):301-5) andtransfusion-related cases have been reported in several jurisdictionsaround the world including the USA (Dodd Id) and Canada (Kain et al.CMAJ. 2001;164(12): 1721-3.). Similar foci of human infection withclosely-related Babesia species can be found on several other continentsincluding Europe and Asia (Gray Pol J Microbiol. 2004;53 Suppl:55-60,Ahmed et al. Parasitol Res. 2002;88(13 Suppl 1):S51-5) and it is verylikely that this family of protozoa is ubiquitous but goes unrecognizedin many regions of the world. Babesia species are major pathogens ofcattle throughout the world (Herwalt In Strickland GT (ed) Hunter'sTropical Medicine and Emerging Infectious Diseases—Eighth Edition.Saunders: Philadelphia 2000. Pp68890.).

Babesiosis is normally a monophasic, malaria-like illness with anincubation period of one to several weeks and duration of 5-15 days(longer following blood transfusion). Like malaria, Babesia targets thehuman erythrocyte for replication. Unlike malaria, there is no acute orchronic liver stage of babesiosis and the parasite life-cycle in humansis restricted to the red blood cell (Herwalt Id, Krause et al. Med ClinNorth Am. 2002;86(2):361-73). Many subjects experience babesiosisinfection with only mild and transient symptoms and the disease goesunrecognized. Typical symptoms are non-specific and include fever,chills and malaise. In the absence of a defined-tick exposure, the indexof suspicion must be quite high to pursue the diagnosis in mostsubjects. In the large majority of subjects with defined babesiosis,recovery from infection is uncomplicated and complete. A small number ofBabesia-infected subjects can suffer more severe and even fatal illness.The commonest risk factor for severe disease is anatomic or functionalabsence of the spleen (eg: post-splenctomy, sickle cell anemia). Thisorgan is largely responsible for removing parasitized RBCs and, in itsabsence, parasitemia can reach very high levels resulting in massivehemolysis, end-organ failure and shock. Rarely, persistent parastemiacan occur (Krause et al. Engl J Med. 1998;339(3):160-5). Furthermore, asmall number of subjects with defined tick exposure and either serologicor microscopic confirmation of infection fail to resolve some or all oftheir symptoms after disappearance of the parasite from their RBC.Although these observations have caused some investigators to speculateabout a chronic form of babesiosis (Sherr MedHypotheses.2004;63(4):609-15), the mechanism(s) by which Babesia couldpersist are, as yet, poorly understood.

A definitive diagnosis of babesiosis is best made either by directmicroscopic identification of parasitized RBCs or by seroconversion withacute and convalescent sera. Microscopic examination can be highlyspecific in skilled hands but this test is subject to both lowsensitivity and low specificity under routine laboratory conditions(Krause 2002, Med Hypotheses. 2004;63(4):609-15). In North America,serologic testing is largely based on indirect immunofluorescence (IFA)performed by a limited number of laboratories in the USA (eg: CDC, someState laboratories in endemic areas). Furthermore, all IFA tests arenotoriously operator-dependent (ie: subjective) and non-specific. As aresult, single serologic measures for babesiosis often give confusingresults.

Accordingly, presently, no optimal test is available for the diagnosisof babesia. Furthermore, there is currently no screening test forbabesiosis that is appropriate for use in the blood system. Several EIAassays have been reported in the literature (Krause et al. J Infect Dis.1994; 169(4):923-6, Houghton et al. Transfusion. 2002;42(11):1488-96,Loa et al. Curr Microbiol. 2004;49(6):385-9) as well as immunoblots(Ryan et al. Clin Diagn Lab Immunol. 2001 November;8(6):1177-80) butthese assays have not yet been commercialized and likely suffer the samelimitations of sensitivity and specificity as the IFA-based assays. Aneed exits for new methods of detecting babesiosis in a subject. Thisinvention is directed to this and other ends.

BRIEF SUMMARY

The present invention provides, inter alia, biomarkers that aredifferentially present in subjects with babesia. In addition, thepresent invention provides methods of using the biomarkers to qualifybabesia in a subject or in a biological sample taken from a subject,including a sample of scrum, blood or other donated tissue. As such, theinvention provides biomarkers that represent fill length proteins orfragments of proteins expressed in infected individuals by a parasite ofthe Babesia family, the pathogen responsible for babesia.

The biomarkers can be used, inter alia, to qualify babesia status,determine the course of babesia, monitor the response to treatment by adrug used to treat babesia, and /or determine a treatment regimen forbabesia. The babesia can be caused by protozoan parasites of the Babesiamicroti family, the Babesia divergens family, or other species in theBabesia family.

In one aspect, the present invention provides a method for qualifyingbabesia status in a subject, the method comprising: (a) measuring atleast one biomarker in a biological sample from the subject, wherein theat least one biomarker is selected from the group consisting of thebiomarkers of Tables 1-3; and (b) correlating the measurement withbabesia status. In one embodiment, the biological sample is a serumsample.

The at least one biomarker can be selected from the group consisting ofbiomarkers of molecular masses of about 2.8, 2.9, 3, 3.1, 3.2, 3.6, 3.8,4, 4.1, 4.2, 4.3, 4.8, 4.9, 6.4, 7, 7.1, 7.2, 7.3, 7.5, 7.7, 7.9, 8.7,8.8, 8.9, 10, 10.1, 10.2, 10.3, 10.4, 10.9, 11, 11.2, 11.3, 11.6, 11.8,11.9, 12.6, 12.7, 12.8, 12.9, 13, 13.1, 13.2, 13.6, 13.8, 14.1, 14.4,14.7, 15.1, 15.6, 15.9, 16.5, 16.7, 17.3, 17.8, 21.9, 22.2, 22.3, 23.5,23.6, 25.5, 25.8, 28.1, 28.2, 33.1, 33.2, 33.3, 34.1, 36.1, 39.8, 43.4,44.2, 44.3, 44.8, 45.1, 46.1, 47.7, 51, 53, 53.6, 60.6, 62.4, 66.9, 79,18.1, 19.2, 22.3, 26.5, 39.6, 39.9, 40.1, 41.3, 43.2, 43.6, 44.2, 44.4,44.6, 45.2, 44.7, 50, 50.5, 51.2, 51.5, 51.9, 52.5, 52.7, 58.9, 59.1,59.6, 59.8, 60.5, 61.6, 61.9, 62.3, 62.8, 64, 66.3, 66.6, 78.5, 79,79.2, 79.5, 79.6, 99.3, 99.6, 110.2, 131.8, 133.5, 134.6, 146.6, 167.8,168, and 196.4 kDa and any combination thereof.

The at least one biomarker can be selected from the group consisting ofbiomarkers of molecular masses of about2.8, 2.9, 3, 3.2, 7.1, 7.2, 7.3,7.5, 7.7, 7.9, 8.9, 14.1, 15.6, 39.8, 44.2, 53.6, 60.6, 62.4, and 79 kDaand any combination thereof. The at least one biomarker can be selectedfrom the group consisting of biomarkers of molecular masses of about11.8, 12.6, 12.7, 12.8, 12.9, 13, 13.1, 13.2, 18.1, 19.2, 26.5, 39.9,43.6, 51.5, 59.8, 62.8, 79 and 146.6 kDa and any combination thereof.The at least one biomarker can be selected from the group consisting ofbiomarkers of molecular masses of about 2.9, 10.2, 10.3, 13.6, 14.7,15.9, 43.2, 44.2, 44.4 and 79.6 kDa and any combination thereof. The atleast one biomarker can be selected from the group consisting ofbiomarkers of molecular masses of about 16.5, 16.7, 39.6, 40.1, 41.3,58.9, 59.6, 60.5, 61.9, 62.8, 64, 66.6, and 79.2 kDa and any combinationthereof. The at least one biomarker can be selected from the groupconsisting of biomarkers of molecular masses of about 3.6, 4.8, 14.1,28.1, and 28.2 kDa. The at least one biomarker can be selected from thegroup consisting of biomarkers of molecular masses of about 10.4, 23.5,25.8, and 44.4 kDa and any combination thereof. The at least onebiomarker can be selected from the group consisting of biomarkers ofmolecular masses of about 2.9, 3.8, 4.2, 4.9, 6.4, 7, 14.1, 25.5, 44.7,45.2, 59.1, 61.6, 62.3, 79.5, and 99.6 kDa and any combination thereof.The at least one biomarker can be selected from the group consisting ofbiomarkers of molecular masses of about 13, 44.6, 133.5, and 168 kDa andany combination thereof. The at least one biomarker can be selected fromthe group consisting of biomarkers of molecular masses of about 3.1, 4,4.1, 8.8, 34.1, 36.1, 44.8, 46.1, 47.7, and 66.9 kDa and any combinationthereof. The at least one biomarker can be selected from the groupconsisting of biomarkers of molecular masses of about 10, 10.1, 10.9,11, 11.2, 11.3, 11.6, 11.9, 43.4, 44.2, 50, 51.9, 52.5, and 134.6 kDaand any combination thereof. The at least one biomarker can be selectedfrom the group consisting of biomarkers of molecular masses of about 4,4.1, 6.4, 8.7, 14.4, 15.1, 17.3, 33.2, 45.1, and 53 kDa and anycombination thereof. The at least one biomarker can be selected from thegroup consisting of biomarkers of molecular masses of about 10, 10.1,12.6, 21.9, 22.3, 23.6, 33.1, 50.5, 51.2, 167.8 kDa and any combinationthereof. The at least one biomarker can be selected from the groupconsisting of biomarkers of molecular masses of about 4.1 and 4.3 kDaand any combination thereof. The at least one biomarker can be selectedfrom the group consisting of biomarkers of molecular masses of about11.6, 17.8, 22.3, and 52.7 kDa and any combination thereof. The at leastone biomarker can be selected from the group consisting of biomarkers ofmolecular masses of about 13.8, 22.2, 33.3, 44.3 kDa and any combinationthereof. The at least one biomarker can be selected from the groupconsisting of biomarkers of molecular masses of about 51, 66.3, 78.5,99.3, 110.2, 131.8, 196.4 kDa and any combination thereof. It will beunderstood that any combination of the biomarkers described herein canbe measured using the methods described herein.

In some embodiments, the at least one biomarker is selected from thegroup consisting of biomarkers of molecular masses of about 22, 28, 33,44, and 146 kDa and any combination thereof. In some embodiments, the atleast one biomarker is selected from the group consisting of biomarkersof molecular masses of about 7.6, 8.9, 28.1 and 44.4 kDa and anycombination thereof. In some embodiments, the at least one biomarker isselected from the group consisting of biomarkers of molecular masses ofabout 7.2 and 7.3 kDa and any combination thereof. In some embodiments,each of the biomarkers having a molecular mass of about 22, 28, 33, 44,and 146 kDa is measured.

In some embodiments, the at least one biomarker is selected from thegroup consisting of biomarkers of molecular masses of about 3, 4, 7, 15,22, 36, 48, 51, 62, and 135 kDa and any combination thereof. In someembodiments, the at least one biomarker is selected from the groupconsisting of biomarkers of molecular masses of about 2.9, 3.6, 7, 14.7,15.1, 22.3, 36.1, 47.7, 51.2, 51.5, 61.9, and 134.6 kDa and anycombination thereof. In some embodiments, the at least one biomarker isselected from the group consisting of biomarkers of molecular masses ofabout 2.86, 3.57, 6.96, 14.72, 15.16, 22.29, 36.08, 47.71, 51.18, 51.54,61.95, and 134.61 kDa and any combination thereof.

In some embodiments, the at least one biomarker is a protein or fragmentthereof as provided in Table 3 and Table P. In certain embodiments, theat least one biomarker is represented by SEQ ID NOS:1-22.

In one embodiment, the at least one biomarker is measured by capturingthe biomarker on an adsorbent of a SELDI probe and detecting thecaptured biomarkers by laser desorption-ionization mass spectrometry. Incertain embodiments, the adsorbent is a cation exchange adsorbent,whereas in other embodiments, the adsorbent is a metal chelationadsorbent. In another embodiment, the at least one biomarker is measuredby immunoassay.

In another embodiment, the correlating is performed by a softwareclassification algorithm. In a further embodiment, babesia status isselected from chronically infected versus uninfected. In yet anotherembodiments, babesia status is selected from chronically infected statusversus acutely infected disease status, chronically infectedasymptomatic status versus chronically affected with symptoms, oracutely infected status versus healthy uninfected status. In stillanother embodiment, babesia status is selected from babesia versushealthy.

In yet another embodiment, the method further comprises managing subjecttreatment based on the status. If the measurement correlates withbabesia, then managing subject treatment comprises administering to apatient drugs selected from a group consisting of, but not necessarilylimited to, drugs such as quinine, clindamycin and combinations thereof.

In a further embodiment, the method further comprises measuring the atleast one biomarker after subject management.

In another aspect, the present invention provides a method comprisingmeasuring at least one biomarker in a sample from a subject, wherein theat least one biomarker is selected from the group consisting of thebiomarkers set forth in Tables 1-3. In one embodiment, the sample is aserum sample.

In still another aspect, the present invention provides a kitcomprising: (a) a solid support comprising at least one capture reagentattached thereto, wherein the capture reagent binds at least onebiomarker from a first group consisting of the biomarkers set forth inTable 1, Table 2 and Table 3; and (b) instructions for using the solidsupport to detect the at least one biomarker set forth in Table 1, Table2 and Table 3.

In other embodiments, the kit additionally comprises (c) a containercontaining at least one of the biomarkers of Table 1, Table 2 and Table3.

In yet a further aspect, the present invention provides a softwareproduct, the software product comprising: (a) code that accesses dataattributed to a sample, the data comprising measurement of at least onebiomarker in the sample, the biomarker selected from the groupconsisting of the biomarkers of Table 1, Table 2 and Table 3; and (b)code that executes a classification algorithm that classifies babesiastatus of the sample as a function of the measurement.

In one embodiment, the classification algorithm classifies babesiastatus of the sample as a function of the measurement of a biomarkerselected from the biomarkers of Tables 1-3.

In other aspects, the present invention provides purified biomoleculesselected from the biomarkers set forth in Table 1, Table 2 and Table 3and, additionally, methods comprising detecting a biomarker set forth inTable 1, Table 2 and Table 3 by mass spectrometry or immunoassay.

In yet another embodiment, the method further comprises testing andqualifying stocks of blood based on the status of blood which has beentested according to the methods described herein. If the measurementstaken from blood samples correlate with babesia, then the management ofblood stocks comprises decontamination of the infected blood bytreatment of the infected blood with purification agents available toone skilled in the art. Alternatively, the infected blood may bediscarded or destroyed and only stocks of blood which have not testedpositively for babesia are retained.

In one aspect, the present invention provides a method for qualifyingbabesia status in a subject in comparison to the status of a differentparasitic, the method comprising: (a) measuring at least one biomarkerin a biological sample from the subject, wherein the at least onebiomarker specifically indicates the presence of babesia and does notindicate the presence of a different parasitic infection; and (b)correlating the measurement with babesia status in comparison to thestatus of a different parasitic infection. In one embodiment, thebiological sample is a serum sample. In a preferred embodiment of thismethod, the at least one biomarker is selected from the group ofbiomarkers of Table 1-3. In still another preferred embodiment, theparasitic infection includes, but is not limited to, Africantrypanosomiasis (sleeping sickness), malaria and Chagas disease.

In another aspect, the present invention provides a method formonitoring the course of progression of babesia in a patient comprising:(a) measuring at least one biomarker in a first biological sample fromthe patient, wherein the at least one biomarker specifically indicatesthe presence of babesia; (b) measuring the at least one biomarker in asecond biological sample from the subject, wherein the second biologicalsample was obtained from the subject after the first biological sample;and (c) correlating the measurements with the progression or regressionof babesia in the subject. In one embodiment, the at least one biomarkeris selected from the group consisting of the biomarkers of Tables 1-3.

Other features, objects and advantages of the invention and itspreferred embodiments will become apparent from the detaileddescription, examples and claims that follow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a graphical representation of the differential signalintensity of the 7.2 and 7.3 kDa biomarkers in subjects having babesiaversus healthy subjects, subjects having African trypanosomiasis(sleeping sickness), subjects having Chagas Disease, and subjects havingMalaria. The biomarkers are characterized by their mass-to-charge ratioas determined by mass spectrometry.

FIG. 2 provides a scanned gel image of total control versus totalbabesia infected sera.

FIG. 3 shows a representation of the differential signal intensity ofthe 51.5 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 4 shows a representation of the differential signal intensity ofthe 14.7 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 5 shows a representation of the differential signal intensity ofthe 61.9 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 6 shows a representation of the differential signal intensity ofthe 7 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 7 shows a representation of the differential signal intensity ofthe 36.1 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 8 shows a representation of the differential signal intensity ofthe 47.7 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 9 shows a representation of the differential signal intensity ofthe 134.6 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 10 shows a representation of the differential signal intensity ofthe 15.2 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 11 shows a representation of the differential signal intensity ofthe 51.2 kDA Biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

FIG. 12 shows a representation of the differential signal intensity ofthe 22.3 kDA biomarker in subjects having babesia versus non-babesia(control), subjects having babesia versus healthy subjects, and subjectshaving babesia versus subjects having flu-like symptoms. The biomarkeris characterized by its mass-to-charge ratio as determined by massspectrometry and is in daltons.

DETAILED DESCRIPTION 1. Introduction

A biomarker is an organic biomolecule which is differentially present ina sample taken from a subject of one phenotypic status (e.g., having adisease) as compared with another phenotypic status (e.g., not havingthe disease). A biomarker is differentially present between differentphenotypic statuses if the mean or median expression level of thebiomarker in the different groups is calculated to be statisticallysignificant. Common tests for statistical significance include, amongothers, t-test, ANOVA, Kruskal-Wallis, Wilcoxon, Mann-Whitney and oddsratio. Biomarkers, alone or in combination, provide measures of relativerisk that a subject belongs to one phenotypic status or another.Therefore, they are useful as markers for disease (diagnostics),therapeutic effectiveness of a drug (theranostics) and drug toxicity.

2. Biomarkers for Babesia

2.1. Biomarkers

This invention provides, inter alia, polypeptide-based biomarkers thatare differentially present in subjects having babesia, in particular,and particularly that are differentially expressed in subjects infectedwith babesia versus non uninfected individuals (e.g., control, healthy,benign condition or other disease state). The biomarkers arecharacterized by mass-to-charge ratio as determined by massspectrometry, by the shape of their spectral peak in time-of-flight massspectrometry and by their binding characteristics to adsorbent surfaces.These characteristics provide one method to determine whether aparticular detected biomolecule is a biomarker of this invention. Thesecharacteristics represent inherent characteristics of the biomoleculesand not process limitations in the manner in which the biomolecules arediscriminated. In one aspect, this invention provides these biomarkersin isolated form.

The biomarkers of Tables 1 and 2 were discovered using SELDI technologyemploying ProteinChip® arrays from Ciphergen Biosystems, Inc. (Fremont,Calif.) (“Ciphergen”). Serum samples were collected from subjectsdiagnosed with babesia and subjects diagnosed as healthy as well assubjects diagnosed with other kinetoplastidae infections (Non-babesia),such as African sleeping sickness, Chagas disease, and malaria or otherconditions such as lyme disease or a flu-like condition. The sampleswere fractionated by anion exchange chromatography. Fractionated sampleswere applied to SELDI biochips and spectra of polypeptides in thesamples were generated by time-of-flight mass spectrometry on aCiphergen PBS IIc mass spectrometer. The spectra thus obtained wereanalyzed by Ciphergen Express™ Data Manager Software with BiomarkerWizard and Biomarker Pattern Software from Ciphergen Biosystems, Inc.The mass spectra for each group were subjected to scatter plot analysis.A Mann-Whitney test analysis was employed to compare babesia and controlgroups for each protein cluster in the scatter plot, and proteins wereselected that differed significantly (p<0.05) between the two groups.This method is described in more detail in the Example Section.

The biomarkers thus discovered arc presented in Tables 1 and 2 (theprotocol for the data obtained. is further described below and. inSection 9, in the Examples, under SELDI ANALYSIS). The heading of eachcolumn refers to chromatographic fraction in which the biomarker isfound, the type of biochip to which the biomarker binds and the washconditions.

TABLE 1 F1CSL F1CSH F1ISL F1ISH F3CSL F3CSH F4ISL F4ISH 2.8 kDa 11.8 kDa 2.9 kDa 16.5 kDa  3.6 kDa 10.4 kDa 13.8 kDa   51 kDa   3 kDa 12.6 kDa10.2 16.7 kDa  4.8 kDa 23.5 kDa 22.2 kDa  66.3 kDa 3.2 kDa 12.7 kDa 10.339.6 kDa 14.1 kDa 25.8 kDa 33.3 kDa  78.5 kDa 7.1 kDa 12.8 kDa 13.6 40.1kDa 28.1 kDa 44.4 kDa 44.3 kDa  99.3 kDa 7.2 kDa 12.9 kDa 14.7 kDa 41.3kDa 28.2 kDa 110.2 kDa 7.3 kDa   13 kDa 15.9 kDa 58.9 kDa 131.8 kDa 7.5kDa 13.1 kDa 43.2 kDa 59.6 kDa 196.4 kDa 7.7 kDa 13.2 kDa 44.2 kDa 60.5kDa 8.9 kDa 18.1 kDa 44.4 kDa 61.9 kDa 14.1 kDa  19.2 kDa 79.6 kDa 62.8kDa 15.6 kDa  26.5 kDa   64 kDa 39.8 kDa  39.9 kDa 66.6 kDa 44.2 kDa 43.6 kDa 79.2 kDa 53.6 kDa  51.5 kDa 60.6 kDa  59.8 kDa 62.4 kDa  62.8kDa  79 kDa   79 kDa 146.6 kDa  * The biomarkers in bold were furtheridentified as significant by Biomarker Patent Software

TABLE 2 F5CSL F5CSH F5ISL F5ISH F6CSL F6CSH F6ISL F6ISH  2.9 kDa   13kDa  3.1 kDa   10 kDa   4 kDa   10 kDa 4.1 kDa 11.6 kDa  3.8 kDa  44.6kDa   4 kDa 10.1 kDa  4.1 kDa 10.1 kDa 4.3 kDa 17.8 kDa  4.2 kDa 133.5kDa  4.1 kDa 10.9 kDa  6.4 kDa 12.6 kDa 22.3 kDa  4.9 kDa   168 kDa  8.8kDa   11 kDa  8.7 kDa 21.9 kDa 52.7 kDa  6.4 kDa 34.1 kDa 11.2 kDa 14.4kDa 22.3 kDa   7 kDa 36.1 kDa 11.3 kDa 15.1 kDa 23.6 kDa 14.1 kDa 44.8kDa 11.6 kDa 17.3 kDa 33.1 kDa 25.5 kDa 46.1 kDa 11.9 kDa 33.2 kDa 50.5kDa 44.7 kDa 47.7 kDa 43.5 kDa 45.1 kDa 51.2 kDa 45.2 kDa 66.9 kDa 44.2kDa   53 kDa 167.8 kDa  59.1 kDa   50 kDa 61.6 kDa 51.9 kDa 62.3 kDa52.5 kDa 79.5 kDa 134.6 kDa  99.6 kDa * The biomarkers in bold werefurther identified as significant by Biomarker Patent Software

The biomarkers of Table 1 and Table 2 are characterized by theirmass-to-charge ratio as determined by mass spectrometry. Themass-to-charge ratio of each biomarker in Table 1 and Table 2 are inkDa. The mass-to-charge ratios were determined from mass spectragenerated on a Ciphergen Biosystems, Inc. PBS IIc mass spectrometer.This instrument has a mass accuracy of about +/−0.15 percent.Additionally, the instrument has a mass resolution of about 400 to 1000m/dm, where m is mass and dm is the mass spectral peak width at 0.5 peakheight. The mass-to-charge ratio of the biomarkers was determined usingBiomarker Wizard™ software (Ciphergen Biosystems, Inc.). BiomarkerWizard assigns a mass-to-charge ratio to a biomarker by clustering themass-to-charge ratios of the same peaks from all the spectra analyzed,as determined by the PBSIIc, taking the maximum and minimummass-to-charge-ratio in the cluster, and dividing by two. Accordingly,the masses provided reflect these specifications.

The identity of certain of the biomarkers of Tables 1 and 2 of thisinvention has been determined and is indicated in Table P in theExamples section. For biomarkers whose identify has been determined, thepresence of the biomarker can be determined by methods known in the artother than mass spectrometry.

The biomarkers of this invention can be further characterized by theshape of their spectral peak in time-of-flight mass spectrometry.

The biomarkers of this invention are further characterized by theirbinding properties on chromatographic surfaces.

The biomarkers of Table 3 were discovered using differential gelelectrophoresis followed by protein identification by matrix-assistedlaser desorption/ionization mass spectrometry (DIGE and MALDI-TOFMS).Serum samples were collected from subjects diagnosed with babesia andsubjects diagnosed as normal (not having babesia). This method isdescribed in more detail in the Example Section.

The biomarkers thus discovered are presented in Table 3.

TABLE 3 Avg. Ratio Avg. Ratio Protein Total Pools Sub-PoolsAlpha1-Antitrypsin (4 spots)   3.95 ± 0.97   5.88 ± 5.35 Chain A,Cleaved* (4 spots) −2.16 ± 0.36 −3.16 ± 2.26 Haptoglobin Beta Chain* (5spots) −1.46 ± 0.12 −2.00 ± 1.04 Alpha Chain (4 spots) −1.81 ± 0.4 −3.50 ± 2.77 Hemoglobin Beta Chain (3 spots)   1.90 ± 0.69   5.19 ± 3.68Apolipoprotein A-IV precursor −2.12 ± 0.01 −3.54 ± 2.79 Unknown spot#940   2.61 ± 0.63 absent Proapolipoprotein* −2.47 ± 0.78 −3.35 ± 2.11Unknown spot #1098   1.70 ± 0.09   1.99 ± 0.33 Immunoglobulins gamma-1heavy chain   3.92 ± 1.29 3.99 heavy constant alpha 1   1.45 ± 0.05  1.87 ± 0.58 M heavy chain*   1.50 ± 0.29 1.94 CD5 Antigen-Like   1.46± 0.14   1.85 ± 0.14 Alpha2-HS Glycoprotein* −2.26 ± 0.06 −1.62 ± 0.53Complement Component 4A   1.88 ± 0.69 3.52 Unknown spot #1309 −1.71 ±0.13 −2.00   Vitamin D-Binding Protein −1.84 ± 0.18 −2.29 ± 0.77 HumanSerum Albumin (2 spots) −1.92 ± 0.21 −2.01 ± 0.61 Complement Factor B*−1.28 ± 0.14 −1.52 ± 0.15 Unknown spot #1560 −1.92 ± 0.26 −1.97 ± 0.1 Annexin V −1.47 ± 0.24 absent

The identity of certain of the biomarkers of Table 3 of this inventionhas been determined and is indicated in Table 3. For biomarkers whoseidentify has been determined, the presence of the biomarker can bedetermined by methods known in the art other than mass spectrometry.

Because the biomarkers of Tables 1 and 2 are characterized bymass-to-charge ratio and binding properties, they can be detected bymass spectrometry without knowing their specific identity. The identityof certain of the biomarkers of Tables 1-3 is known. If desired,biomarkers whose identity is not determined can be identified by, forexample, determining the amino acid sequence of the polypeptides. Forexample, a biomarker can be peptide-mapped with a number of enzymes,such as trypsin or V8 protease, and the molecular weights of thedigestion fragments can be used to search databases for sequences thatmatch the molecular weights of the digestion fragments generated by thevarious enzymes. Alternatively, protein biomarkers can be sequencedusing tandem MS technology. In this method, the protein is isolated by,for example, gel electrophoresis. A band containing the biomarker is cutout and the protein is subject to protease digestion. Individual proteinfragments are separated by a first mass spectrometer. The fragment isthen subjected to collision-induced cooling, which fragments the peptideand produces a polypeptide ladder. A polypeptide ladder is then analyzedby the second mass spectrometer of the tandem MS. The difference inmasses of the members of the polypeptide ladder identifies the aminoacids in the sequence. An entire protein can be sequenced this way, or asequence fragment can be subjected to database mining to find identitycandidates.

The preferred biological source for detection of the biomarkers isserum. However, in other embodiments, the biomarkers are detected inurine and other biological samples.

The biomarkers of this invention are biomolecules. Accordingly, thisinvention provides these biomolecules in isolated form. The biomarkerscan be isolated from biological fluids, such as serum. They can beisolated by any method known in the art, based on both their mass andtheir binding characteristics. For example, a sample comprising thebiomolecules can be subject to chromatographic fractionation, asdescribed herein, and subject to further separation by, e.g., acrylamidegel electrophoresis. Knowledge of the identity of the biomarker alsoallows their isolation by immunoaffinity chromatography.

2.2. Biomarkers and Modified Forms of a Protein

Proteins frequently exist in a sample in a plurality of different forms.These forms can result from either, or both, of pre- andpost-translational modification. Pre-translational modified formsinclude allelic variants, slice variants and RNA editing forms.Post-translationally modified forms include forms resulting fromproteolytic cleavage (e.g., fragments of a parent protein),glycosylation, phosphorylation, lipidation, oxidation, methylation,cysteinylation, sulphonation and acetylation. When detecting ormeasuring a protein in a sample, the ability to differentiate betweendifferent forms of a protein depends upon the nature of the differenceand the method used to detect or measure. For example, immunologicalmethods of detection typically cannot distinguish between differentforms of a protein that contain the same epitope or epitopes to whichthe antibody or antibodies are directed. In diagnostic assays, theinability to distinguish different forms of a protein has little impactwhen the forms detected by the particular method used are equally goodbiomarkers as any particular form. However, when a particular form (or asubset of particular forms) of a protein is a better biomarker than thecollection of modified forms detected together by a particular method,the power of the assay may suffer. In this case, it is useful to employan assay method that distinguishes between forms of a protein and thatspecifically detects and measures a desired modified form or forms ofthe protein. Distinguishing different forms of an analyte orspecifically detecting a particular form of an analyte is referred to as“resolving” the analyte.

The collection of analytes detected in an assay and the ability toresolve modified forms of a protein of course depends on the methodologyused. For example, an immunoassay using a monoclonal antibody willdetect all forms of a protein containing the eptiope and will notdistinguish between them. However, a sandwich immunoassay that uses twoantibodies directed against different epitopes on a protein will detectall forms of the protein that contain both epitope and will not detectthose forms that contain only one of the epitopes. Accordingly thismethod can be useful when the modified forms differ in a terminal aminoacid and one of the antibodies is directed to the terminus of one ofthese forms.

Preferably, the biospecific capture reagent is bound to a solid phase,such as a bead, a plate, a membrane or a chip. Methods of couplingbiomolecules, such as antibodies, to a solid phase are well known in theart. They can employ, for example, bifunctional linking agents, or thesolid phase can be derivatized with a reactive group, such as an epoxideor an imidizole, that will bind the molecule on contact. Biospecificcapture reagents against different target proteins can be mixed in thesame place, or they can be attached to solid phases in differentphysical or addressable locations. For example, one can load multiplecolumns with derivatized beads, each column able to capture a singleprotein cluster. Alternatively, one can pack a single column withdifferent beads derivatized with capture reagents against a variety ofprotein clusters, thereby capturing all the analytes in a single place.Accordingly, antibody-derivatized bead-based technologies, such as xMAPtechnology of Luminex (Austin, Tex.) can be used to detect the proteinclusters. However, the biospecific capture reagents must be specificallydirected toward the members of a cluster in order to differentiate them.

Mass spectrometry is a particularly powerful resolving methodologybecause different forms of a protein typically have different masses andcan be differentiated by mass spectrometry. One useful methodologycombines mass spectrometry with immunoassay. First, a biospecificcapture reagent (e.g., an antibody, aptamer or Affibody that recognizesthe biomarker and modified forms of it) is used to capture the biomarkerof interest. Preferably, the biospecific capture reagent is bound to asolid phase, such as a bead, a plate, a membrane or a chip. Afterunbound materials are washed away, the captured analytes are detectedand/or measured by mass spectrometry. (This method also will also resultin the capture of protein interactors that arc bound to the proteins orthat arc otherwise recognized by antibodies and that, themselves, can bebiomarkers.) Then, the captured proteins can be detected by SELDI massspectrometry or by eluting the proteins from the capture reagent anddetecting the eluted proteins by traditional MALDI, SELDI or any otherionization method for mass spectrometry (e.g., electrospray).

Thus, when reference is made herein to detecting a particular protein orto measuring the amount of a particular protein, it means detecting andmeasuring the protein with or without resolving modified forms ofprotein. For example, the step of “measuring Apolipoprotein A-IVprecursor” includes measuring Apolipoprotein A-IV precursor by meansthat do not differentiate between various forms of the protein (e.g.,certain immunoassays) as well as by means that differentiate some formsfrom other forms or that measure a specific form of the protein. Incontrast, when it is desired to measure a particular form or forms of aprotein, the particular form (or forms) is specified. For example,“measuring M7.065159” or a biomarker of 7.065159 kDa means measuring itin a way that distinguishes it from forms of the protein that do nothave the characteristic properties identified in Tables 1 and 2.

3. Detection of Biomarkers for Babesia

The biomarkers of this invention can be detected by any suitable method.Detection paradigms that can be employed to this end include opticalmethods, electrochemical methods (voltametry and amperometrytechniques), atomic force microscopy, and radio frequency methods, e.g.,multipolar resonance spectroscopy. Illustrative of optical methods, inaddition to microscopy, both confocal and non-confocal, are detection offluorescence, luminescence, chemiluminescence, absorbance, reflectance,transmittance, and birefringence or refractive index (e.g., surfaceplasmon resonance, ellipsometry, a resonant mirror method, a gratingcoupler waveguide method or interferometry).

In one embodiment, a sample is analyzed by means of a biochip. Biochipsgenerally comprise solid substrates and have a generally planar surface,to which a capture reagent (also called an adsorbent or affinityreagent) is attached. Frequently, the surface of a biochip comprises aplurality of addressable locations, each of which has the capturereagent bound there.

Protein biochips are biochips adapted for the capture of polypeptides.Many protein biochips are described in the art. These include, forexample, protein biochips produced by Ciphergen Biosystems, Inc.(Fremont, Calif.), Zyomyx (Hayward, Calif.), Invitrogen (Carlsbad,Calif.), Biacore (Uppsala, Sweden) and Procognia (Berkshire, UK).Examples of such protein biochips arc described in the following patentsor published patent applications: U.S. Pat. No. 6,225,047 (Hutchens &Yip); U.S. Pat. No. 6,537,749 (Kuimelis and Wagner); U.S. Pat. No.6,329,209 (Wagner et al.); PCT International Publication No. WO 00/56934(Englert et al.); PCT International Publication No. WO 03/048768(Boutell et al.) and U.S. Pat. No. 5,242,828 (Bergstrom et al.).

3.1. Detection by Mass Spectrometry

In a preferred embodiment, the biomarkers of this invention are detectedby mass spectrometry, a method that employs a mass spectrometer todetect gas phase ions. Examples of mass spectrometers aretime-of-flight, magnetic sector, quadrupole filter, ion trap, ioncyclotron resonance, electrostatic sector analyzer and hybrids of these.

In a further preferred method, the mass spectrometer is a laserdesorption/ionization Mass spectrometer. In laser desorption/ionizationmass spectrometry, the analytes are placed on the surface of a massspectrometry probe, a device adapted to engage a probe interface of themass spectrometer and to present an analyte to ionizing energy forionization and introduction into a mass spectrometer. A laser desorptionmass spectrometer employs laser energy, typically from an ultravioletlaser, but also from an infrared laser, to desorb analytes from asurface, to volatilize and ionize them and make them available to theion optics of the mass spectrometer.

3.1.1. SELDI

A preferred mass spectrometric technique for use in the invention is“Surface Enhanced Laser Desorption and Ionization” or “SELDI,” asdescribed, for example, in U.S. Pat. No. 5,719,060 and U.S. Pat. No.6,225,047, both to Hutchens and Yip. This refers to a method ofdesorption/ionization gas phase ion spectrometry (e.g., massspectrometry) in which an analyte (here, one or more of the biomarkers)is captured on the surface of a SELDI mass spectrometry probe. There areseveral versions of SELDI.

One version of SELDI is called “affinity capture mass spectrometry.” Italso is called “Surface-Enhanced Affinity Capture” or “SEAC”. Thisversion involves the use of probes that have a material on the probesurface that captures analytes through a non-covalent affinityinteraction (adsorption) between the material and the analyte. Thematerial is variously called an “adsorbent,” a “capture reagent,” an“affinity reagent” or a “binding moiety.” Such probes can be referred toas “affinity capture probes” and as having an “adsorbent surface.” Thecapture reagent can be any material capable of binding an analyte. Thecapture reagent is attached to the probe surface by physisorption orchemisorption. In certain embodiments the probes have the capturereagent already attached to the surface. In other embodiments, theprobes are pre-activated and include a reactive moiety that is capableof binding the capture reagent, e.g., through a reaction forming acovalent or coordinate covalent bond. Epoxide and acyl-imidizole areuseful reactive moieties to covalently bind polypeptide capture reagentssuch as antibodies or cellular receptors. Nitrilotriacetic acid andiminodiacetic acid are useful reactive moieties that function aschelating agents to bind metal ions that interact non-covalently withhistidine containing peptides. Adsorbents are generally classified aschromatographic adsorbents and biospecific adsorbents.

“Chromatographic adsorbent” refers to an adsorbent material typicallyused in chromatography. Chromatographic adsorbents include, for example,ion exchange materials, metal chelators (e.g., nitrilotriacetic acid oriminodiacetic acid), immobilized metal chelates, hydrophobic interactionadsorbents, hydrophilic interaction adsorbents, dyes, simplebiomolecules (e.g., nucleotides, amino acids, simple sugars and fattyacids) and mixed mode adsorbents (e.g., hydrophobicattraction/electrostatic repulsion adsorbents).

“Biospecific adsorbent” refers to an adsorbent comprising a biomolecule,e.g., a nucleic acid molecule (e.g., an aptamer), a polypeptide, apolysaccharide, a lipid, a steroid or a conjugate of these (e.g., aglycoprotein, a lipoprotein, a glycolipid, a nucleic acid (e.g.,DNA)-protein conjugate). In certain instances, the biospecific adsorbentcan be a macromolecular structure such as a multiprotein complex, abiological membrane or a virus. Examples of biospecific adsorbents areantibodies, receptor proteins and nucleic acids. Biospecific adsorbentstypically have higher specificity for a target analyte thanchromatographic adsorbents. Further examples of adsorbents for use inSELDI can be found in U.S. Pat. No. 6,225,047. A “bioselectiveadsorbent” refers to an adsorbent that binds to an analyte with anaffinity of at least 10⁻⁸ M.

Protein biochips produced by Ciphergen Biosystems, Inc. comprisesurfaces having chromatographic or biospecific adsorbents attachedthereto at addressable locations. Ciphergen ProteinChip® arrays includeNP20 (hydrophilic); H4 and H50 (hydrophobic); SAX-2, Q-10 and LSAX-30(anion exchange); WCX-2, CM-10 and LWCX-30 (cation exchange); IMAC-3,IMAC-30 and IMAC 40 (metal chelate); and PS-10, PS-20 (reactive surfacewith acyl-imidizole, epoxide) and PG-20 (protein G coupled throughacyl-imidizole). Hydrophobic ProteinChip arrays have isopropyl ornonylphenoxy-poly(ethylene glycol)methacrylate functionalities. Anionexchange ProteinChip arrays have quaternary ammonium functionalities.Cation exchange ProteinChip arrays have carboxylate functionalities.Immobilized metal chelate ProteinChip arrays have nitrilotriacetic acidfunctionalities that adsorb transition metal ions, such as copper,nickel, zinc, and gallium, by chelation. Preactivated ProteinChip arrayshave acyl-imidizole or epoxide functional groups that can react withgroups on proteins for covalent binding.

Such biochips are further described in: U.S. Pat. No. 6,579,719(Hutchens and Yip, “Retentate Chromatography,” Jun. 17, 2003); U.S. Pat.No. 6,897,072 (Rich et al., “Probes for a Gas Phase Ion Spectrometer,”May 24, 2005); U.S. Pat. No. 6,555,813 (Beecher et al., “Sample Holderwith Hydrophobic Coating for Gas Phase Mass Spectrometer,” Apr. 29,2003); U.S. patent application Ser. No. U.S. 2003/0032043 A1 (Pohl andPapanu, “Latex Based Adsorbent Chip,” Jul. 16, 2002); and PCTInternational Publication No. WO 03/040700 (Um et al., “HydrophobicSurface Chip,” May 15, 2003); U.S. patent application Ser. No. US2003/0218130 A1 (Boschetti et al., “Biochips With Surfaces Coated WithPolysaccharide-Based Hydrogels,” Apr. 14, 2003) and U.S. patentapplication Ser. No. 60/448,467, entitled “Photocrosslinked HydrogelSurface Coatings” (Huang et al., filed Feb. 21, 2003).

In general, a probe with an adsorbent surface is contacted with thesample for a period of time sufficient to allow the biomarker orbiomarkers that may be present in the sample to bind to the adsorbent.After an incubation period, the substrate is washed to remove unboundmaterial. Any suitable washing solutions can be used; preferably,aqueous solutions are employed. The extent to which molecules remainbound can be manipulated by adjusting the stringency of the wash. Theelution characteristics of a wash solution can depend, for example, onpH, ionic strength, hydrophobicity, degree of chaotropism, detergentstrength, and temperature. Unless the probe has both SEAC and SENDproperties (as described herein), an energy absorbing molecule then isapplied to the substrate with the bound biomarkers.

The biomarkers bound to the substrates are detected in a gas phase ionspectrometer such as a time-of-flight mass spectrometer. The biomarkersare ionized by an ionization source such as a laser, the generated ionsarc collected by an ion optic assembly, and then a mass analyzerdisperses and analyzes the passing ions. The detector then translatesinformation of the detected ions into mass-to-charge ratios. Detectionof a biomarker typically will involve detection of signal intensity.Thus, both the quantity and mass of the biomarker can be determined.

Another version of SELDI is Surface-Enhanced Neat Desorption (SEND),which involves the use of probes comprising energy absorbing moleculesthat are chemically bound to the probe surface (“SEND probe”). Thephrase “energy absorbing molecules” (EAM) denotes molecules that arecapable of absorbing energy from a laser desorption/ionization sourceand, thereafter, contribute to desorption and ionization of analytemolecules in contact therewith. The EAM category includes molecules usedin MALDI, frequently referred to as “matrix,” and is exemplified bycinnamic acid derivatives, sinapinic acid (SPA), cyano-hydroxy-cinnamicacid (CHCA) and dihydroxybenzoic acid, ferulic acid, andhydroxyacetophenone derivatives. In certain embodiments, the energyabsorbing molecule is incorporated into a linear or cross-linkedpolymer, e.g., a polymethacrylate. For example, the composition can be aco-polymer of α-cyano-4-methacryloyloxycinnamic acid and acrylate. Inanother embodiment, the composition is a co-polymer ofα-cyano-4-methacryloyloxycinnamic acid, acrylate and 3-(tri-ethoxy)silylpropyl methacrylate. In another embodiment, the composition is aco-polymer of α-cyano-4-methacryloyloxycinnamic acid andoctadecylmethacrylate (“C18 SEND”). SEND is further described in U.S.Pat. No. 6,124,137 and PCT International Publication No. WO 03/64594(Kitagawa, “Monomers And Polymers Having Energy Absorbing Moieties OfUse In Desorption/Ionization Of Analytes,” Aug. 7, 2003).

SEAC/SEND is a version of SELDI in which both a capture reagent and anenergy absorbing molecule are attached to the sample presenting surface.SEAC/SEND probes therefore allow the capture of analytes throughaffinity capture and ionization/desorption without the need to applyexternal matrix. The C18 SEND biochip is a version of SEAC/SEND,comprising a C18 moiety which functions as a capture reagent, and a CHCAmoiety which functions as an energy absorbing moiety.

Another version of SELDI, called Surface-Enhanced Photolabile Attachmentand Release (SEPAR), involves the use of probes having moieties attachedto the surface that can covalently bind an analyte, and then release theanalyte through breaking a photolabile bond in the moiety after exposureto light, e.g., to laser light (see, U.S. Pat. No. 5,719,060). SEPAR andother forms of SELDI arc readily adapted to detecting a biomarker orbiomarker profile, pursuant to the present invention.

3.1.2. Other Mass Spectrometry Methods

In another mass spectrometry method, the biomarkers are first capturedon a chromatographic resin having chromatographic properties that bindthe biomarkers. In the present example, this could include a variety ofmethods. For example, one could capture the biomarkers on a cationexchange resin, such as CM Ceramic HyperD F resin, wash the resin, elutethe biomarkers and detect by MALDI. Alternatively, this method could bepreceded by fractionating the sample on an anion exchange resin beforeapplication to the cation exchange resin. In another alternative, onecould fractionate on an anion exchange resin and detect by MALDIdirectly. In yet another method, one could capture the biomarkers on animmuno-chromatographic resin that comprises antibodies that bind thebiomarkers, wash the resin to remove unbound material, elute thebiomarkers from the resin and detect the eluted biomarkers by MALDI orby SELDI. In yet another method, one could isolate the biomarkers usinggel elecrophoresis and detect the biomarkers by MALDI OR SELDI.

3.1.3. Data Analysis

Analysis of analytes by time-of-flight mass spectrometry generates atime-of-flight spectrum. The time-of-flight spectrum ultimately analyzedtypically does not represent the signal from a single pulse of ionizingenergy against a sample, but rather the sum of signals from a number ofpulses. This reduces noise and increases dynamic range. Thistime-of-flight data is then subject to data processing. In Ciphergen'sProteinChip® software, data processing typically includes TOF-to-M/Ztransformation to generate a mass spectrum, baseline subtraction toeliminate instrument offsets and high frequency noise filtering toreduce high frequency noise.

Data generated by desorption and detection of biomarkers can be analyzedwith the use of a programmable digital computer. The computer programanalyzes the data to indicate the number of biomarkers detected, andoptionally the strength of the signal and the determined molecular massfor each biomarker detected. Data analysis can include steps ofdetermining signal strength of a biomarker and removing data deviatingfrom a predetermined statistical distribution. For example, the observedpeaks can be normalized, by calculating the height of each peak relativeto some reference.

The computer can transform the resulting data into various formats fordisplay. The standard spectrum can be displayed, but in one usefulformat only the peak height and mass information are retained from thespectrum view, yielding a cleaner image and enabling biomarkers withnearly identical molecular weights to be more easily seen. In anotheruseful format, two or more spectra are compared, convenientlyhighlighting unique biomarkers and biomarkers that are up- ordown-regulated between samples. Using any of these formats, one canreadily determine whether a particular biomarker is present in a sample.

Analysis generally involves the identification of peaks in the spectrumthat represent signal from an analyte. Peak selection can be donevisually, but software is available, as part of Ciphergen's ProteinChip®software package, that can automate the detection of peaks. In general,this software functions by identifying signals having a signal-to-noiseratio above a selected threshold and labeling the mass of the peak atthe centroid of the peak signal. In one useful application, many spectraare compared to identify identical peaks present in some selectedpercentage of the mass spectra. One version of this software clustersall peaks appearing in the various spectra within a defined mass range,and assigns a mass (M/Z) to all the peaks that are near the mid-point ofthe mass (M/Z) cluster.

Software used to analyze the data can include code that applies analgorithm to the analysis of the signal to determine whether the signalrepresents a peak in a signal that corresponds to a biomarker accordingto the present invention. The software also can subject the dataregarding observed biomarker peaks to classification tree or ANNanalysis, to determine whether a biomarker peak or combination ofbiomarker peaks is present that indicates the status of the particularclinical parameter under examination. Analysis of the data may be“keyed” to a variety of parameters that are obtained, either directly orindirectly, from the mass spectrometric analysis of the sample. Theseparameters include, but are not limited to, the presence or absence ofone or more peaks, the shape of a peak or group of peaks, the height ofone or more peaks, the log of the height of one or more peaks, and otherarithmetic manipulations of peak height data.

3.1.4. General Protocol for SELDI Detection of Biomarkers for Babesia

A preferred protocol for the detection of the biomarkers of thisinvention is as follows. The biological sample to be tested, e.g.,serum, preferably is subject to pre-fractionation before SELDI analysis.This simplifies the sample and improves sensitivity. A preferred methodof pre-fractionation involves contacting the sample with an anionexchange chromatographic material, such as Q HyperD (BioSepra, SA). Thebound materials are then subject to stepwise pH elution using buffers atpH 9, pH 7, pH 5 and pH 4. (The fractions in which the biomarkers areeluted also is indicated in Table 1.) Various fractions containing thebiomarker are collected.

The sample to be tested (preferably pre-fractionated) is then contactedwith an affinity capture probe comprising an cation exchange adsorbent(preferably a WCX ProteinChip array (Ciphergen Biosystems, Inc.)) or anIMAC adsorbent (preferably an IMAC3 ProteinChip array (CiphergenBiosystems, Inc.)), again as indicated in Table 1. The probe is washedwith a buffer that will retain the biomarker while washing away unboundmolecules. A suitable wash for each biomarker is the buffer identifiedin Table 1. The biomarkers are detected by laser desorption/ionizationmass spectrometry.

Alternatively, if antibodies that recognize the biomarker are available,these can be attached to the surface of a probe, such as a pre-activatedPS10 or PS20 ProteinChip array (Ciphergen Biosystems, Inc.). Theseantibodies can capture the biomarkers from a sample onto the probesurface. Then the biomarkers can be detected by, e.g., laserdesorption/ionization mass spectrometry.

3.2. Detection by Immunoassay

In another embodiment of the invention, the biomarkers of the inventionare measured by a method other than mass spectrometry or other thanmethods that rely on a measurement of the mass of the biomarker. In onesuch embodiment that does not rely on mass, the biomarkers of thisinvention are measured by immunoassay. Immunoassay requires biospecificcapture reagents, such as antibodies, to capture the biomarkers.Antibodies can be produced by methods well known in the art, e.g., byimmunizing animals with the biomarkers. Biomarkers can be isolated fromsamples based on their binding characteristics. Alternatively, if theamino acid sequence of a polypeptide biomarker is known, the polypeptidecan be synthesized and used to generate antibodies by methods well knownin the art.

This invention contemplates traditional immunoassays including, forexample, sandwich immunoassays including ELISA or fluorescence-basedimmunoassays, as well as other enzyme immunoassays. Nephelometry is anassay done in liquid phase, in which antibodies are in solution. Bindingof the antigen to the antibody results in changes in absorbance, whichis measured. In the SELDI-based immunoassay, a biospecific capturereagent for the biomarker is attached to the surface of an MS probe,such as a pre-activated ProteinChip array. The biomarker is thenspecifically captured on the biochip through this reagent, and thecaptured biomarker is detected by mass spectrometry.

4. Determination of Subject Babesia Status

4.1. Single Markers

The biomarkers of the invention can be used in diagnostic tests toassess babesia status in a subject, e.g., to diagnose Babesia. Thephrase “Babesia status” includes any distinguishable manifestation ofthe disease, including non-disease. For example, disease statusincludes, without limitation, the presence or absence of disease (e.g.,babesia v. non babesia or Babesia v. other parasitic disease (e.g.,African sleeping sickness, Chagas, malaria)), the risk of developingdisease, the stage of the disease, the progress of disease (e.g.,progress of disease or remission of disease over time) and theeffectiveness or response to treatment of disease. The status of thesubject may inform the practitioner about what status set is beingdistinguished. For example, a subject that presents with signs of aparasitic disease could be classed into Babseia v. non-Babesia parasiticdisease, while a person exposed to a situation in which Babesiainfection is possible and who is presenting with signs of Babesiainfection could be classified into Babesia v. non-Babesia. Based on thisstatus, further procedures may be indicated, including additionaldiagnostic tests or therapeutic procedures or regimens.

The power of a diagnostic test to correctly predict status is commonlymeasured as the sensitivity of the assay, the specificity of the assayor the area under a receiver operated characteristic (“ROC”) curve.Sensitivity is the percentage of true positives that are predicted by atest to be positive, while specificity is the percentage of truenegatives that arc predicted by a test to be negative. An ROC curveprovides the sensitivity of a test as a function of 1-specificity. Thegreater the area under the ROC curve, the more powerful the predictivevalue of the test. Other useful measures of the utility of a test arepositive predictive value and negative predictive value. Positivepredictive value is the percentage of people who test positive that areactually positive. Negative predictive value is the percentage of peoplewho test negative that are actually negative.

The biomarkers of this invention show a statistical difference indifferent babesia statuses of at least p≦0.05, p≦10⁻², p≦10⁻³, p≦10 ⁻⁴or p≦10⁻⁵. Diagnostic tests that use these biomarkers alone or incombination show a sensitivity and specificity of at least 75%, at least80%, at least 85%, at least 90%, at least 95%, at least 98% and about100%.

Each biomarker listed in Tables 1, 2 and 3 is differentially present inbabesia, and, therefore, each is individually useful in aiding in thedetermination of babesia status. The method involves, first, measuringthe selected biomarker in a subject sample using the methods describedherein, e.g., capture on a SELDI biochip followed by detection by massspectrometry and, second, comparing the measurement with a diagnosticamount or cut-off that distinguishes a positive babesia status from anegative babesia status. The diagnostic amount represents a measuredamount of a biomarker above which or below which a subject is classifiedas having a particular babesia status. For example, if the biomarker isup-regulated compared to normal during babesia, then a measured amountabove the diagnostic cutoff provides a diagnosis of babesia.Alternatively, if the biomarker is down-regulated during babesia, then ameasured amount below the diagnostic cutoff provides a diagnosis ofbabesia. As is well understood in the art, by adjusting the particulardiagnostic cut-off used in an assay, one can increase sensitivity orspecificity of the diagnostic assay depending on the preference of thediagnostician. The particular diagnostic cut-off can be determined, forexample, by measuring the amount of the biomarkers in a statisticallysignificant number of samples from subjects with the different babesiastatuses, as was done here, and drawing the cut-off to suit thediagnostician's desired levels of specificity and sensitivity.

4.2. Combinations of Markers

While individual biomarkers are useful diagnostic biomarkers, it hasbeen found that a combination of biomarkers can provide greaterpredictive value of a particular status than single biomarkers alone.Specifically, the detection of a plurality of biomarkers in a sample canincrease the sensitivity and/or specificity of the test. A combinationof at least two biomarkers is sometimes referred to as a “biomarkerprofile” or “biomarker fingerprint.”

4.3. Presence of Babesia

In one embodiment, this invention provides methods for determining thepresence or absence of babesia in a subject (status: babesia v. non-babesia). The presence or absence of babesia is determined by measuringthe relevant biomarker or biomarkers and then either submitting them toa classification algorithm or comparing them with a reference amountand/or pattern of biomarkers that is associated with the particular risklevel.

4.4. Determining Risk of Developing Disease

In one embodiment, this invention provides methods for determining therisk of developing disease in a subject. Biomarker amounts or patternsare characteristic of various risk states, e.g., high, medium or low.The risk of developing a disease is determined by measuring the relevantbiomarker or biomarkers and then either submitting them to aclassification algorithm or comparing them with a reference amountand/or pattern of biomarkers that is associated with the particular risklevel

4.5. Determining Stage of Disease

In one embodiment, this invention provides methods for determining thestage of disease in a subject. Each stage of the disease has acharacteristic amount of a biomarker or relative amounts of a set ofbiomarkers (a pattern). The stage of a disease is determined bymeasuring the relevant biomarker or biomarkers and then eithersubmitting them to a classification algorithm or comparing them with areference amount and/or pattern of biomarkers that is associated withthe particular stage.

4.6. Determining Course (Progression/Remission) of Disease

In one embodiment, this invention provides methods for determining thecourse of disease in a subject. Disease course refers to changes indisease status over time, including disease progression (worsening) anddisease regression (improvement). Over time, the amounts or relativeamounts (e.g., the pattern) of the biomarkers changes. Therefore, thetrend of these markers, either increased or decreased over time towarddiseased or non-diseased indicates the course of the disease.Accordingly, this method involves measuring one or more biomarkers in asubject at least two different time points, e.g., a first time and asecond time, and comparing the change in amounts, if any. The course ofdisease is determined based on these comparisons.

4.7. Subject Management

In certain embodiments of the methods of qualifying babesia status, themethods further comprise managing subject treatment based on the status.Such management includes the actions of the physician or cliniciansubsequent to determining babesia status. For example, if a physicianmakes a diagnosis of babesia, then a certain regime of treatment, suchas prescription or administration of quinine, clindamycin or acombination thereof, might follow. Alternatively, a diagnosis of non-babesia might be followed with further testing to determine a specificdisease that might the patient might be suffering from. Also, if thediagnostic test gives an inconclusive result on babesia status, furthertests may be called for.

The methods described herein can be used in combination with and othertests and/or methods that are used to qualify babesia status in asubject. For example, in certain aspects, the methods described hereinare used to determine whether or not a subject has an increasedlikelihood of having babesia. These methods can be used in combinationwith other tests that are useful for either diagnosing babesia in asubject or ruling out other diagnoses.

Additional embodiments of the invention relate to the communication ofassay results or diagnoses or both to technicians, physicians orpatients, for example. In certain embodiments, computers will be used tocommunicate assay results or diagnoses or both to interested parties,e.g., physicians and their patients. In some embodiments, the assayswill be performed or the assay results analyzed in a country orjurisdiction which differs from the country or jurisdiction to which theresults or diagnoses are communicated.

In a preferred embodiment of the invention, a diagnosis based on thepresence or absence in a test subject of any the biomarkers of Table 1,2 or 3 is communicated to the subject as soon as possible after thediagnosis is obtained. The diagnosis may be communicated to the subjectby the subject's treating physician. Alternatively, the diagnosis may besent to a test subject by email or communicated to the subject by phone.A computer may be used to communicate the diagnosis by email or phone.In certain embodiments, the message containing results of a diagnostictest may be generated and delivered automatically to the subject using acombination of computer hardware and software which will be familiar toartisans skilled in telecommunications. One example of ahealthcare-oriented communications system is described in U.S. Pat. No.6,283,761; however, the present invention is not limited to methodswhich utilize this particular communications system. In certainembodiments of the methods of the invention, all or some of the methodsteps, including the assaying of samples, diagnosing of diseases, andcommunicating of assay results or diagnoses, may be carried out indiverse (e.g., foreign) jurisdictions.

4.8. Determining Therapeutic Efficacy of Pharmaceutical Drug

In another embodiment, this invention provides methods for determiningthe therapeutic efficacy of a pharmaceutical drug. These methods areuseful in performing clinical trials of the drug, as well as monitoringthe progress of a patient on the drug. Therapy or clinical trialsinvolve administering the drug in a particular regimen. The regimen mayinvolve a single dose of the drug or multiple doses of the drug overtime. The doctor or clinical researcher monitors the effect of the drugon the patient or subject over the course of administration. If the drughas a pharmacological impact on the condition, the amounts or relativeamounts (e.g., the pattern or profile) of the biomarkers of thisinvention changes toward a non-disease profile. One can follow thecourse of the amounts of these biomarkers in the subject during thecourse of treatment. Accordingly, this method involves measuring one ormore biomarkers in a subject receiving drug therapy, and correlating theamounts of the biomarkers with the disease status of the subject. Oneembodiment of this method involves determining the levels of thebiomarkers at least two different time points during a course of drugtherapy, e.g., a first time and a second time, and comparing the changein amounts of the biomarkers, if any. For example, the biomarkers can bemeasured before and after drug administration or at two different timepoints during drug administration. The effect of therapy is determinedbased on these comparisons. If a treatment is effective, then thebiomarkers will trend toward normal, while if treatment is ineffective,the biomarkers will trend toward disease indications. If a treatment iseffective, then the biomarkers will trend toward normal, while iftreatment is ineffective, the biomarkers will trend toward diseaseindications.

5. Generation of Classification Algorithms for Qualifying Babesia Status

In some embodiments, data derived from the spectra (e.g., mass spectraor time-of-flight spectra) that are generated using samples such as“known samples” can then be used to “train” a classification model. A“known sample” is a sample that has been pre-classified. The data thatare derived from the spectra and are used to form the classificationmodel can be referred to as a “training data set.” Once trained, theclassification model can recognize patterns in data derived from spectragenerated using unknown samples. The classification model can then beused to classify the unknown samples into classes. This can be useful,for example, in predicting whether or not a particular biological sampleis associated with a certain biological condition (e.g., diseased versusnon-diseased).

The training data set that is used to form the classification model maycomprise raw data or pre-processed data. In some embodiments, raw datacan be obtained directly from time-of-flight spectra or mass spectra,and then may be optionally “pre-processed” as described above.

Classification models can be formed using any suitable statisticalclassification (or “learning”) method that attempts to segregate bodiesof data into classes based on objective parameters present in the data.Classification methods may be either supervised or unsupervised.Examples of supervised and unsupervised classification processes arcdescribed in Jain, “Statistical Pattern Recognition: A Review”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol. 22, No.1, January 2000, the teachings of which are incorporated by reference.

In supervised classification, training data containing examples of knowncategories are presented to a learning mechanism, which learns one ormore sets of relationships that define each of the known classes. Newdata may then be applied to the learning mechanism, which thenclassifies the new data using the learned relationships. Examples ofsupervised classification processes include linear regression processes(e.g., multiple linear regression (MLR), partial least squares (PLS)regression and principal components regression (PCR)), binary decisiontrees (e.g., recursive partitioning processes such as CART -classification and regression trees), artificial neural networks such asback propagation networks, discriminant analyses (e.g., Bayesianclassifier or Fischer analysis), logistic classifiers, and supportvector classifiers (support vector machines).

A preferred supervised classification method is a recursive partitioningprocess. Recursive partitioning processes use recursive partitioningtrees to classify spectra derived from unknown samples. Further detailsabout recursive partitioning processes are provided in U.S. patentapplication Ser. No. 2002 0138208 A1 to Paulse et al., “Method foranalyzing mass spectra.”

In other embodiments, the classification models that are created can beformed using unsupervised learning methods. Unsupervised classificationattempts to learn classifications based on similarities in the trainingdata set, without pre-classifying the spectra from which the trainingdata set was derived. Unsupervised learning methods include clusteranalyses. A cluster analysis attempts to divide the data into “clusters”or groups that ideally should have members that are very similar to eachother, and very dissimilar to members of other clusters. Similarity isthen measured using some distance metric, which measures the distancebetween data items, and clusters together data items that are closer toeach other. Clustering techniques include the MacQueen's K-meansalgorithm and the Kohonen's Self-Organizing Map algorithm.

Learning algorithms asserted for use in classifying biologicalinformation are described, for example, in PCT International PublicationNo. WO 01/31580 (Barnhill et al., “Methods and devices for identifyingpatterns in biological systems and methods of use thereof”), U.S. patentapplication Ser. No. 2002 0193950 A1 (Gavin et al., “Method or analyzingmass spectra”), U.S. patent application Ser. No.2003 0004402 A1 (Hitt etal., “Process for discriminating between biological states based onhidden patterns from biological data”), and U.S. patent application Ser.No. 2003 0055615 A1 (Zhang and Zhang, “Systems and methods forprocessing biological expression data”).

The classification models can be formed on and used on any suitabledigital computer. Suitable digital computers include micro, mini, orlarge computers using any standard or specialized operating system, suchas a Unix, Windows™ or Linux™ based operating system. The digitalcomputer that is used may be physically separate from the massspectrometer that is used to create the spectra of interest, or it maybe coupled to the mass spectrometer.

The training data set and the classification models according toembodiments of the invention can be embodied by computer code that isexecuted or used by a digital computer. The computer code can be storedon any suitable computer readable media including optical or magneticdisks, sticks, tapes, etc., and can be written in any suitable computerprogramming language including C, C++, visual basic, etc.

The learning algorithms described above are useful both for developingclassification algorithms for the biomarkers already discovered, or forfinding new biomarkers for babesia. The classification algorithms, inturn, form the base for diagnostic tests by providing diagnostic values(e.g., cut-off points) for biomarkers used singly or in combination.

6. Compositions of Matter

In another aspect, this invention provides compositions of matter basedon the biomarkers of this invention.

In one embodiment, this invention provides biomarkers of this inventionin purified form. Purified biomarkers have utility as antigens to raiseantibodies. Purified biomarkers also have utility as standards in assayprocedures. As used herein, a “purified biomarker” is a biomarker thathas been isolated from other proteins and peptides, and/or othermaterial from the biological sample in which the biomarker is found.Biomarkers may be purified using any method known in the art, including,but not limited to, mechanical separation (e.g., centrifugation),ammonium sulphate precipitation, dialysis (including size-exclusiondialysis), size-exclusion chromatography, affinity chromatography,anion-exchange chromatography, cation-exchange chromatography, andmethal-chelate chromatography. Such methods may be performed at anyappropriate scale, for example, in a chromatography column, or on abiochip.

In another embodiment, this invention provides a biospecific capturereagent, optionally in purified form, that specifically binds abiomarker of this invention. In one embodiment, the biospecific capturereagent is an antibody. Such compositions are useful for detecting thebiomarker in a detection assay, e.g., for diagnostics.

In another embodiment, this invention provides an article comprising abiospecific capture reagent that binds a biomarker of this invention,wherein the reagent is bound to a solid phase. For example, thisinvention contemplates a device comprising bead, chip, membrane,monolith or microtiter plate derivatized with the biospecific capturereagent. Such articles are useful in biomarker detection assays.

In another aspect this invention provides a composition comprising abiospecific capture reagent, such as an antibody, bound to a biomarkerof this invention, the composition optionally being in purified form.Such compositions are useful for purifying the biomarker or in assaysfor detecting the biomarker.

In another embodiment, this invention provides an article comprising asolid substrate to which is attached an adsorbent, e.g., achromatographic adsorbent or a biospecific capture reagent, to which isfurther bound a biomarker of this invention. In one embodiment, thearticle is a biochip or a probe for mass spectrometry, e.g., a SELDIprobe. Such articles are useful for purifying the biomarker or detectingthe biomarker.

7. Kits for Detection of Biomarkers for Babesia

In another aspect, the present invention provides kits for qualifyingbabesia status, which kits are used to detect biomarkers according tothe invention. In one embodiment, the kit comprises a solid support,such as a chip, a microtiter plate or a bead or resin having a capturereagent attached thereon, wherein the capture reagent binds a biomarkerof the invention. Thus, for example, the kits of the present inventioncan comprise mass spectrometry probes for SELDI, such as ProteinChip®arrays. In the case of biospecfic capture reagents, the kit can comprisea solid support with a reactive surface, and a container comprising thebiospecific capture reagent.

The kit can also comprise a washing solution or instructions for makinga washing solution, in which the combination of the capture reagent andthe washing solution allows capture of the biomarker or biomarkers onthe solid support for subsequent detection by, e.g., mass spectrometry.The kit may include more than type of adsorbent, each present on adifferent solid support.

In a further embodiment, such a kit can comprise instructions forsuitable operational parameters in the form of a label or separateinsert. For example, the instructions may inform a consumer about how tocollect the sample, how to wash the probe or the particular biomarkersto be detected.

In yet another embodiment, the kit can comprise one or more containerswith biomarker samples, to be used as standard(s) for calibration.

8. Use of Biomarkers for B abesia in Screening Assays and Methods ofTreating Babesia

The methods of the present invention have other applications as well.For example, the biomarkers can be used to screen for compounds thatmodulate the expression of the biomarkers in vitro or in vivo, whichcompounds in turn may be useful in treating or preventing babesia inpatients. In another example, the biomarkers can be used to monitor theresponse to treatments for babesia. In yet another example, thebiomarkers can be used in heredity studies to determine if the subjectis at risk for developing babesia.

Thus, for example, the kits of this invention could include a solidsubstrate having a hydrophobic function, such as a protein biochip(e.g., a Ciphergen H50 ProteinChip array, e.g., ProteinChip array) and asodium acetate buffer for washing the substrate, as well as instructionsproviding a protocol to measure the biomarkers of this invention on thechip and to use these measurements to diagnose babesia.

Compounds suitable for therapeutic testing may be screened initially byidentifying compounds which interact with one or more biomarkers listedin Table 1, 2 or 3. By way of example, screening might includerecombinantly expressing a biomarker listed in Table 1, 2 or 3,purifying the biomarker, and affixing the biomarker to a substrate. Testcompounds would then be contacted with the substrate, typically inaqueous conditions, and interactions between the test compound and thebiomarker are measured, for example, by measuring elution rates as afunction of salt concentration. Certain proteins may recognize andcleave one or more biomarkers of Table 1, 2 or 3, in which case theproteins may be detected by monitoring the digestion of one or morebiomarkers in a standard assay, e.g., by gel electrophoresis of theproteins.

In a related embodiment, the ability of a test compound to inhibit theactivity of one or more of the biomarkers of Table 1, 2 or 3 may bemeasured. One of skill in the art will recognize that the techniquesused to measure the activity of a particular biomarker will varydepending on the function and properties of the biomarker. For example,an enzymatic activity of a biomarker may be assayed provided that anappropriate substrate is available and provided that the concentrationof the substrate or the appearance of the reaction product is readilymeasurable. The ability of potentially therapeutic test compounds toinhibit or enhance the activity of a given biomarker may be determinedby measuring the rates of catalysis in the presence or absence of thetest compounds. The ability of a test compound to interfere with anon-enzymatic (e.g. structural) function or activity of one of thebiomarkers of Table 1, 2 or 3 may also be measured. For example, theself-assembly of a multi-protein complex which includes one of thebiomarkers of Table 1, 2 or 3 may be monitored by spectroscopy in thepresence or absence of a test compound. Alternatively, if the biomarkeris a non-enzymatic enhancer of transcription, test compounds whichinterfere with the ability of the biomarker to enhance transcription maybe identified by measuring the levels of biomarker-dependenttranscription in vivo or in vitro in the presence and absence of thetest compound.

Test compounds capable of modulating the activity of any of thebiomarkers of Table 1, 2 or 3 may be administered to patients who aresuffering from or are at risk of developing babesia. For example, theadministration of a test compound which increases the activity of aparticular biomarker may decrease the risk of babesia in a patient ifthe activity of the particular biomarker in vivo prevents theaccumulation of proteins for babesia. Conversely, the administration ofa test compound which decreases the activity of a particular biomarkermay decrease the risk of babesia in a patient if the increased activityof the biomarker is responsible, at least in part, for the onset ofbabesia.

In an additional aspect, the invention provides a method for identifyingcompounds useful for the treatment of disorders such as babesia whichare associated with increased levels of modified forms of the biomarkersin Table 1, 2 or 3. For example, in one embodiment, cell extracts orexpression libraries may be screened for compounds which catalyze thecleavage of a full-length biomarker to form truncated forms of thebiomarker. In one embodiment of such a screening assay, cleavage of thebiomarker may be detected by attaching a fluorophore to the biomarkerwhich remains quenched when the biomarker is uncleaved but whichfluoresces when the protein is cleaved. Alternatively, a version offull-length biomarker modified so as to render the amide bond betweenamino acids x and y uncleavable may be used to selectively bind or“trap” the cellular protesase which cleaves full-length biomarker atthat site in vivo. Methods for screening and identifying proteases andtheir targets are well-documented in the scientific literature, e.g., inLopez-Ottin et al. (Nature Reviews, 3:509-519 (2002)).

In yet another embodiment, the invention provides a method for treatingor reducing the progression or likelihood of a disease, e.g., babesia,which is associated with the increased levels of a truncated biomarker.For example, after one or more proteins have been identified whichcleave the full-length biomarker, combinatorial libraries may bescreened for compounds which inhibit the cleavage activity of theidentified proteins. Methods of screening chemical libraries for suchcompounds are well-known in art. See, e.g., Lopez-Otin et al. (2002).Alternatively, inhibitory compounds may be intelligently designed basedon the structure of the biomarker.

At the clinical level, screening a test compound includes obtainingsamples from test subjects before and after the subjects have beenexposed to a test compound. The levels in the samples of one or more ofthe biomarkers listed in Table 1, 2 or 3 may be measured and analyzed todetermine whether the levels of the biomarkers change after exposure toa test compound. The samples may be analyzed by mass spectrometry, asdescribed herein, or the samples may be analyzed by any appropriatemeans known to one of skill in the art. For example, the levels of oneor more of the biomarkers listed in Table 1, 2 or 3 may be measureddirectly by Western blot using radio- or fluorescently-labeledantibodies which specifically bind to the biomarkers. Alternatively,changes in the levels of mRNA encoding the one or more biomarkers may bemeasured and correlated with the administration of a given test compoundto a subject. In a further embodiment, the changes in the level ofexpression of one or more of the biomarkers may be measured using invitro methods and materials. For example, human tissue cultured cellswhich express, or are capable of expressing, one or more of thebiomarkers of Table 1, 2 or 3 may be contacted with test compounds.Subjects who have been treated with test compounds will be routinelyexamined for any physiological effects which may result from thetreatment. In particular, the test compounds will be evaluated for theirability to decrease disease likelihood in a subject. Alternatively, ifthe test compounds are administered to subjects who have previously beendiagnosed with babesia, test compounds will be screened for theirability to slow or stop the progression of the disease.

9. EXAMPLES 9.1. Example 1 Discovery of Biomarkers for Babesia

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested to persons skilled -in the art andare to be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference in theirentirety for all purposes.

Two, complimentary approaches to identifying potential biomarkers forthe diagnosis of human babesiosis have been taken: 1) SELDI-based and 2)2-D gels (DIGE technology). Based on estimated molecular weight, thereis an overlap of at least 5 biomarkers identified by both approaches(MWs 22, 28, 33, 44 and 146 kDa).

SELDI Analysis

A total of 20 positive and 20 negative for babesia and positive samplesfrom others protozoan parasites were examined: African sleeping sickness(n=10), Chagas disease (n=10) and malaria (n=10).

The serum samples fractionation was done using 96 well filtration platecontaining Q Ceramic HyperD F according to the manufacturer'sinstructions (Ciphergen, Fremont, Calif., Cat. # K100-0007) on a BioMek2000 (Beckman Coulter). Briefly, 200 μl of rehydration buffer (50 mMTris-HCl, pH 9) was added 2 times to each well and equilibrated 3 timeswith U1 buffer [1 M urea, 2% (w/v) CHAPS, 50 mM Tris-HC1, pH 9]. Twentymicroliter of each serum were mixed with 30 μl of U9 buffer [9 M urea,2% (w/v) CHAPS, 50 mM Tris-HCl, pH 9] in a 96 wells plate v-bottom for20 min. The sample was then diluted with 50 μl of U1 buffer. One hundredmicroliters of the diluted serum sample were applied to each well,incubated and mixed on MicroMix (Beckman Coulter) for 30 min. Theflow-through was collected by vacuum filtration into v-bottommicroplates. The anion-exchange resin was incubated with an additional100 μl of Tris-HCl buffer [50 mM Tris-HCl, pH 9, 0.1% (w/v) OGP] for 10min at room temperature with shaking. The wash was collected by vacuumfiltration. This procedure was repeated two times with 100 μl each ofappropriate buffers with decreasing pH (pH 7, 5, 4, 3 and organic). Thefinal wash was performed with an organic wash buffer containing 33%(v/v) isopropanol and 16.7% (v/v) acetonitrile in 0.1% trifluoroaceticacid (TFA). Fractionated samples were stored at −80 C. until analysis.

The following chip binding protocol was followed and the samples wereprocessed using an IMAC-3 ProteinChip Array according to the protocolbelow:

Chip Binding Protocol Weak Cation Exchange (WCX2) ProteinChip ArrayMaterials:

-   Bioprocessor-   WCX-2 chip-   Vortex-   CM low stringency buffer-   Deionized water-   EAM solution    -   1. Assemble the WCX-2 protein chip in the bioprocessor.    -   2. Add 150 ul of CM low stringency buffer to each well.    -   3. Vortex for 5 minutes (speed 100 rpm) at room temperature.    -   4. Remove the buffer from the wells.    -   5. Repeat steps 2 to 3 for a total of 2 washes.    -   6. Add 90 ul of CM low stringency buffer to each well.    -   7. Add 10 ul of sample (fractions) to each well.    -   8. Vortex for 30 minutes (speed 100 rpm) at room temperature.    -   9. Remove the samples from the wells.    -   10. Wash each well with 150 ul CM low stringency buffer.    -   11. Vortex for 5 minutes (100 rpm).    -   12. Repeat twice for a total of three buffer washes.    -   13. Remove the washing buffer from the wells and rinse each well        with deionized water.    -   14. Drain the wells and remove the chip from the bioprocessor.    -   15. Allow the chip to air dry.    -   16. Apply 0.5-1 ul of EAM solution per spot twice.    -   17. Allow to air dry after each application.    -   18. Analyze the chip.        Processing Samples using an IMAC-3 ProteinChip Array

Material:

-   Bioprocessors-   IMAC Chips-   Pap Pen-   Votex (VWR VX-2500 Multitube Vortexer)

IMAC3 Chip Buffer:

-   A) Binding Buffer: 100 mM Sodium Phosphate+0.5M NaCl pH 7.0+0.1%    Triton X 20-   B) Charging Buffer (Copper): 100 nM CUSO₄+0.1% Triton X 20-   C) Neutralizing Buffer: 100 mM NaAcetate pH 4.0+0.1% Triton X 20    -   1. Place Chip in bioprocessor    -   2. Load IMAC chips with copper: Apply 50 μl/well of 100 mM CuSO₄    -   3. Vortex 5 min (speed 100 rpm) at room temperature    -   4. Remove CuSO₄    -   5. Wash with water 120 μl/well    -   6. Vortex 5 min (speed 100 rpm)    -   7. Neutralize chips: Add 50 μl/well of 100 mM NaAcetate pH 4.0    -   8. Remove solution    -   9. Wash with water 120 μl/well    -   10. Vortex 5 min (speed 100 rpm)    -   11. Repeat steps 9 & 10 a further two times    -   12. Equilibrate Chips: Add 120 μl Binding Buffer (PBS/0.5 M        NaCl, pH 7.5)    -   13. Vortex 5 min (100 rpm)    -   14. Bind fractions to chips: Discard waste and add 80 μl Binding        Buffer and 20 μl of fractions (containing samples)    -   15. Vortex 45-60 min (100 rpm)    -   16. Discard and wash (PBS/0.5M NaCl, 150 μl/well)    -   17. Vortex 5 min (100 rpm)    -   18. Repeat steps 16 & 17 a further two times    -   19. Rinse chip with dH₂O (150 μl/well)    -   20. Add Matrix: Remove bioproceesor top and gasket    -   21. Rinse the Chips quickly with dH₂O    -   22. Dry chips    -   23. Circle spots with PAP pen    -   24. Add 0.5 μl SPA to Chips two times (air dry the spots between        addition)        -   Ciphergen normally supplies EAM as 5 mg of dried powder in a            tube.        -   Add 100 μl of 100% Acetonitrile (final concentration 50%            ACN)+50 μl 2%        -   Trifluoroacetic acid (final conc. 0.5% TFA)+50 μl dH₂O.        -   Vortex 1 min (high speed) and leave it in the bunch for 5            min        -   Spin 2 min at high speed to pellet any particulates    -   25. Dry    -   26. Read within 1 hour

Data Acquisition

The ProteinChip Arrays were analyzed in the ProteinChip Biology SystemReader (Model PBS IIc, Ciphergen Biosystems) with an autoloader. Thespectra were collected using two different laser intensities (low andhigh) for each fraction (pH).

The data were analyzed by ProteinChip Software version 3.0 (CiphergenBiosystems), CiphergenExpress version 2.1 and Biomarkers patternSoftware version 2.2. All spectra were subjected to mass calibrationbased on the settings used to collect the data, baseline subtraction andnoise at 2,000 Da for the low energy and 10,000 Da for the high energy.All data were normalized by total ion current normalization for lowintensity (2-100 kDa) and high intensity (10-200 kDa) using an externalcoefficient of 0.2. Signal-to-noise ratio (S/N) was set at 3, with aminimum peak threshold at 10%, a cluster mass window at 0.3% for thefirst pass and S/N at 2 for the same settings for the second pass exceptthe cluster mass set at 2%.

Analysis Using 2D-DIGE and MALDI/TOF MS

A total of 33 sera samples (16 controls, 17 babesia-infected) weretested for biomarker discovery using differential gel electrophoresisfollowed by protein identification by matrix-assisted laserdesorption/ionization mass spectrometry (DIGE and MALDI-TOFMS). Inbrief, protein was isolated from each individual serum specimen and thencombined to generate 4 separate sub-pools for each sera type (i.e., n=4individuals for each babesia and control sub-pool). Each sub-pool wasthen labeled with either Cy3 or Cy5 fluorescent dye, combined with asub-pool from the opposing group (and stained with the other dye) andrun by DIGE. (i.e, There were 4 gels -run in total.) Differences inprotein levels between each group were then determined followingscanning and image analysis (Decyder software, GE Heathcare). A samplecontaining a mix of all specimens was labeled with Cy2 dye and run oneach gel to facilitate gel-to-gel comparisons. Differences in proteinlevels between control and babesia sub-pools were further validatedusing DIGE runs containing a pool of all control specimens versus a poolof all babesia-infected sera (i.e., two gels of the same total poolswith a dye swap for disease versus control). Biomarkers for babesiosiswere identified based on identical increase/decrease trends observed inthe replicate sub-pools (4 gels) and in the total babesia verses totalcontrol pools (2 gels). Visual inspection of all interesting proteinspots was also used to validate the ratios determined by the Decyderprogram for all gels. Protein spots that met the stated criteria werepicked from the gel, digested with trypsin, and identified by eitherMALDI-TOF or LC-MS/MS. A total of 37 protein spots corresponding to 21unique proteins were determined. to either increase/decrease withbabesia-infection in human sera. Many of these potential biomarkers areassociated with an increase in the immune/inflammatory response (alpha1-antitrypsin, immunoglobulins, complement component 4A, complimentfactor B, and CD5-like antigen) or indicative of accelerated hemolysiswith infection (hemoglobin B chain and haptoglobin).

Each serum sample contained approx. 40 μL of serum. A key to thespecimens is as follows:

Babesia negative positive 2004 12-22 1-8, 10, 11 2005 1, 6-8, 10-12 2-5,9

Protein was isolated independently for each serum sample by amethanol/chloroform procedure and the concentrations in each weredetermined by a Bradford Protein Assay. All samples were diluted withthe appropriate buffer to yield a final concentration of 5 mg/mL proteinper specimen. Equal volumes from 4 specimens per sera group werecombined to create 8 sub-pools: 4 sub-pools for control and 4 sub-poolsfor babesia-infected serum. The following table lists the individualsamples that were combined to generate each sub-pool.

Serum Type: Control Babesia Sub-pool #1 2MULA, 3GESP, 4GLES, 1AGRR,6MELD, 7DUGN, 5DUGG 8MCEK Sub-pool #2 12-14, 9AADW 10SCHD, 11MUER, 6, 7Sub-pool #3 15-18 1-4 Sub-pool #4 19-22 5, 8-11

An “all-samples pool” was created by combining equal volumes from theabove sub-pools into a single mix to serve as a control on all DIGEgels. Pools containing all control samples and all disease samples werealso made for each serum type by combining equal volumes from each ofthe four sub-pools from control or babesia sera.

Sample Labeling

Before labeling, the protein concentrations for each sub-pool and poolwere again determined by the Bradford Protein Assay to make sureequivalent levels of protein were employed for DIGE analysis. Protein(50 μg) was then labeled from control and babesia-infected sera witheither Cy3 or Cy5 fluorescent dye and 50 μg protein from the all-samplespool was labeled with Cy2. The Cy3/Cy5 labeling of control/babesia serawas alternated for each gel to avoid any bias that might arise from thelabeling chemistry of a particular dye to specific protein. Controlsub-pool #1 was compared to babesia sub-pool #1, control sub-pool #2 wascompared to babesia sub-pool #2, and so on, while the total control pooland the total babesia pool were compared against each other. (Duplicateexperiments were performed with a dye swap.) When the gel was to be usedto determine protein ID's, additional unlabeled protein (425 μg) fromboth control and babesia sera was spiked in following the labelingreaction. The Cy3-, Cy5- and Cy2-labeled specimens were then combined,reduced with HED, mixed with appropriate pharmalytes, colored withbromophenol blue, and used immediately for DIGE.

DIGE Analysis

Strip rehydration and focusing: Labeled control/babesia/all sample mixwas applied to Amersham Immobiline DryStrips (pH 3-10, 24 cm) for thepurpose of separating proteins based on charge. The strips wererehydrated with protein samples overnight, then run for ≠66,000 Vhr onan IPGphor Isoelectric Focusing Unit (Amersham Biosciences). Followingfocusing, the strips were treated with a reduction solution (DTT in aSDS-equilibration buffer) and an alkylation solution (iodoacetamide inSDS-equilibration buffer). Following reduction and alkylation the stripswere immediately transferred to analytical (all sub-pools) orpreparative (total control/babesia pools) gels for protein sizeseparation.

Analytical gels (control/babesia sub-pools): Immobiline DryStripscontaining the control vs. babesia sub-pools were placed at the top of4, 8-16% acrylamide gradient gels (Jule Biotechnologies) and run at 1.5V per gel overnight. The protein concentration loaded for analyticalgels was sufficient to determine differences in protein levels betweentwo samples, but not enough to obtain protein ID's. Once the dye frontfor each gel reached the bottom of the glass plate, the gels wereremoved and either scanned immediately or fixed (7.5% acetic acid, 30%methanol) washed, and stored overnight (H₂O, 4° C.) then scanned thenext day.

Preparative gels (total control/babesia pools): The protocol for runningpreparative gels is identical to that described above except that 1 mgof total protein is used instead of 0.150 mg. This facilitates proteinidentification from the picked spots. Both the total control vs. totalbabesia gels (i.e., this includes a dye-swap sample) were run on 8-16%acrylamide gradient preparative gels.

Scanning Fluorescently Labeled Gels

Gels were scanned on a Typhoon 9400 Scanner (Amersham). Emissions fromthe three different fluorescent dyes (Cy3, Cy5, and Cy2) were measuredon separate channels using different filters for wavelength and bandpass. This allows for relative protein levels of three differentsamples, which in our case is control/babesia/combination of allsamples, to be measured on the same gel. Furthermore, it is possible toadjust the voltage levels for each dye. Typically, laser intensity wasadjusted such that the signal measured for any protein spot was belowsaturation levels. (See section on the data analysis of preparativegels). The Typhoon Scanner software designates colors for the threefluorescent dyes: Cy3=green, Cy5=red, and Cy2=blue. Images created fromscanned gels show a yellow color for protein spots that do not change inlevels between the two conditions while spots with a green or red colorindicate an increase/decrease in protein levels across the twoconditions. (In our case this is control vs. babesia serum samples.) Animage of one of the scanned preparative gels is included as FIG. 5.

Images of scanned gels were imported into Decyder (Amersham) fordifferential protein analysis. The Decyder software package wasspecifically designed for the DIGE technology and allows for functionsincluding: spot detection and quantification, viewing of spot data(image, 3-D, table and histogram views), comparing spot data frommultiple gels (average ratio, T-test, ANOVA, etc. . . ) and for creatingpick lists. An image of the scanned gel in the previous section is givenas FIG. 6 following spot detection by the Decyder software. As with theprevious gel picture, spots outlined in yellow show no change in proteinlevels across control vs. babesia sera, while red outlined spotsincrease with babesia (>1.5 fold) and green spots decrease (<−1.5 fold)with the disease.

Data analysis of analytical gels. The four control vs. babesia sub-poolgels were processed and the spots detected and matched to determinecommon differentially expressed proteins. Of 728 spots matched betweenthe four gels, 42 spots produced a T-test p-value of less than 0.05 forcontrol vs. babesia. An example of one of these spots is given below asFIGS. 7A-C showing its image in both control and babesia channels, a 3-Drepresentation of spot volume, and the log₂ expression on all foursub-pool gels.

Data analysis of preparative gels. Two preparative gels comparing allcontrol samples vs. all babesia-infected sample were run. Each gelcontained the same samples except that the Cy3/Cy5 labeling of totalcontrol/total babesia was switched for each gel.

Protein Identification Using MALDI/TOF and/or LC/MS

Spots determined to be potential biomarkers for babesia were picked fromone of the preparative gels using an Ettan Spot Picker (Amersham). Thegel plugs were then placed in a 96-well plate and proteins wereextracted and digested overnight with trypsin using an Ettan Digester(Amersham). The resulting peptide mixture was then spotted on a MALDIplate and analyzed using a DE-STR MALDI-TOF mass spectrometer (AppliedBiosystems). The peptide fingerprint for each spot was compared againstthe NCBI database (contains all organisms) using Mascot Daemon software(Matrix Science Ltd.). A positive match for protein identification wasbased on the number of peptides matched to a particular protein, theprotein coverage of the matched peptides, and the error in the observedpeptide masses as compared to the theoretical masses. If a matchcouldn't be obtained with a high degree of confidence from the firstpreparative gel, the same spot was picked from the second preparativegel and processed as before using the same MALDI protocol. If theprotein could still not be identified after the second attempt withMALDI, the peptide mixture was then subjected to LC-MS/MS analysis forpeptide sequencing. The results from LC-MS/MS were again comparedagainst the NCBI database using predetermined criteria for correctprotein identification.

Results

To determine potential biomarkers for babesia, increasing or decreasingtrends across all gels were identified. At medium to high abundance,DIGE is able to differentiate a 20% change in protein levels betweenlabeled samples. a>1.2 fold change in expression (control vs. babesia)was used as the inclusion limit for biomarkers of babesiosis. This>1.2fold change was required to be observed in the majority of the analyticgel comparisons (3 out of 4 gels) and/or in both preparative gels.Protein spots that met the above conditions were then inspected visuallyto confirm the identity of the spot on each gel, to provide additionalvalidation on the determined Decyder ratios, and to get an approximationof MW and pI for the spot to aid in protein identification. More spotswere identified in the over-exposed preparative gels than in theanalytical gels and therefore some changes in spot intensity observed inthe preparative gels were not detected in some/all of the analyticalgels.

In Table 2 are 37 spots corresponding to roughly 21 unique proteins thatare biomarkers for babesiosis. The number of distinct spots identifiedfor each particular protein is given in parentheses. Values in the tableare the average fold change (babesia levels÷control levels) across thetwo preparative gels (total pools) and the four analytical gels(sub-pools)±SD for each unique protein.

A difference in the DIGE experiments is an increase in alpha1-antitrypsin (AAT) levels with babesiosis. Although only 4 AAT spotsare listed in Table 2, there are more spots attributable to this proteinaround ˜45 kDa and pI of ˜5.4 on each gel. AAT is an acute phase proteinand it has been reported that levels can increase up to 4 fold duringinflammation (1). Consistent with its role in the inflammatory process,our group has observed increases in AAT levels for a variety ofdifferent pathologies and in multiple tissue-types. Conversely, thedecrease in the chain A component of AAT may indicate increasedstabilization of the entire protein and therefore cleaved products fromAAT may be reduced as a result.

An increase in hemoglobin and a decrease in haptoglobin levels maybeindicative of hemolysis associated with the pathology of babesiainfection. Both of these proteins had multiple hits in our screen, eachwith the same increasing/decreasing patterns for each separate spot. Anincrease in hemoglobin is consistent with the babesia-mediated lysis oferythrocytes following infection. Haptoblobin functions in the cellulardefense response by binding to and eliminating free hemoglobin in theblood, thereby canceling its toxic effects (increased ROS production,promoting bacterial growth, etc.) in the body. Haptoglobin is removedfrom the blood along with the bound hemoglobin, which may explain thedecrease in the haptoglobin plasma concentration observed in patientswith accclerated hemolysis.

Many of the proteins isolated from the DIGE experiments were associatedwith the immune response and as expected, these immune-related proteinsare present at higher levels in babesia infection. Immunoglobins heavyconstant alpha 1 (IGHAL) and M heavy chain (IgM) were increased ca.1.5-2 fold in babesiosis, while the gamma-1 heavy chain constant region(IGHG1) increased almost 4 fold. Two other proteins, complement factor B(CF) and complement component 4A (C4A), are associated withcomplementation activation of the immune response. The genes that encodethese two proteins are both localized to the major histocompatibilitycomplex (MHC) class TTI region on chromosome 6 (6p21.3). Levels for CD5antigen-like (CD5L), a protein involved in apoptosis and the cellulardefense response, increased ˜1.6 fold with disease. CD5L has been shownto associate with IgM (3), another protein that was isolated in thisscreen. One immune-related protein, apolipoprotein A-IV precursor(APOA4), had lower levels in the babesia-infected sera as compared tothe control (˜2 fold difference in the preparative gels). Visualinspection of the gels showed that the spot corresponding to APOA4 hadthe clearest difference in levels between the two types of sera from theproteins given in Table 1. The precise function of APOA4 is not known,but it is believed to be involved in lipid metabolism (Gao J et al., JBiol Chem. 2005;280(13):12559-66) and the anti-inflammatory response(Vowinkel et al. J Clin. Invest 2004;1 14(2):260-9). This protein couldbe a target for destruction by the babesia parasite, or that the hostitself decreases the levels of this anti-inflammatory protein in orderto combat the infection.

Tables A-P below show the results of a biomarker discovery study.Biomarkers that show a statistical difference in different babesiastatuses of at least p≦0.05 are provided in Tables 1 and 2. Thebiomarkers presented in these tables can be used in all aspects of thepresent invention. F1CSL and F1CSH refers to Fraction 1, WCX2, SPA, Lowor High intensity; F1ISL and F1ISH refer to Fraction 1, IMAC, SPA, Lowor High intensity; F3CSL and F3CSH refer to Fraction 3, WCX2, SPA, Lowor High intensity; F5CSL or F5CSH refer to Fraction 5, WCX2, SPA, Low orHigh intensity; F5ISL and F5ISH refer to Fraction 5, 1MAC, SPA, Low orHigh intensity; F6CSL and F6CSH refer to Fraction 6, WCX2, SPA, Low orHigh intensity; and F6ISL and F6ISH refer to Fraction 6, IMAC, SPA, Lowor High intensity.

TABLE A Biomarkers identified in F1CSL Babesia vs. Babesia Babesia vs.Babesia Healthy vs. vs. Non- Babesia vs. Flu-like with/Lyme Flu-likeBabesia 1 babesia Healthy symptoms Disease symptoms vs. Babesia 2 M/

P P P P P P (av

value ROC value ROC value ROC value ROC value ROC value ROC D

0.005 0.234 0.024 0.263 0.027 0.232 0.572 0.563 0.482 0.583 0.897 0.5292

0.194 0.357 0.013 0.239 0.282 0.627 0.785 0.514 0.027 0.182 0.651 0.558

0.024 0.705 0.034 0.706 0.178 0.696 0.082 0.312 0.366 0.667 0.439 0.596

0.000 0.868 0.000 0.872 0.021 0.768 0.232 0.370 0.366 0.629 0.699 0.400

0.003 0.786 0.005 0.800 0.095 0.696 0.034 0.254 0.763 0.583 0.897 0.496

0.001 0.807 0.001 0.848 0.085 0.699 0.572 0.447 0.315 0.674 0.439 0.625

0.092 0.684 0.246 0.634 0.106 0.696 0.057 0.254 0.366 0.409 0.561 0.558

0.005 0.766 0.031 0.753 0.018 0.804 0.107 0.312 0.132 0.265 0.017 0.1673

0.057 0.643 0.024 0.753 0.667 0.587 0.005 0.196 0.070 0.720 1.000 0.4673

0.538 0.459 0.450 0.615 0.021 0.192 0.107 0.312 0.035 0.765 0.897 0.5253

0.026 0.725 0.028 0.729 0.236 0.659 0.005 0.196 0.688 0.538 0.478 0.4293

0.042 0.684 0.014 0.777 0.667 0.554 0.014 0.196 0.108 0.720 0.561 0.4293

0.024 0.725 0.049 0.682 0.118 0.696 0.249 0.389 0.841 0.538 0.796 0.5293

0.011 0.275 0.026 0.259 0.085 0.297 0.173 0.630 0.688 0.409 0.561 0.3963

0.019 0.316 0.004 0.176 0.706 0.446 0.098 0.698 0.012 0.136 0.561 0.4293

0.019 0.295 0.003 0.172 0.829 0.478 0.148 0.650 0.027 0.182 0.333 0.3673

0.116 0.643 0.038 0.706 0.957 0.518 0.516 0.370 0.088 0.758 1.000 0.4964

0.006 0.766 0.002 0.824 0.333 0.623 0.068 0.312 0.228 0.667 0.606 0.4924

0.044 0.336 0.019 0.263 0.590 0.406 0.516 0.601 0.191 0.318 1.000 0.5004

0.005 0.725 0.001 0.848 0.389 0.623 0.034 0.225 0.016 0.856 0.606 0.5964

0.026 0.725 0.080 0.682 0.067 0.732 0.009 0.225 0.615 0.409 0.606 0.5624

0.014 0.254 0.018 0.263 0.178 0.304 0.572 0.572 0.920 0.485 0.651 0.4624

0.044 0.684 0.013 0.777 0.747 0.551 0.082 0.283 0.044 0.803 0.366 0.5624

0.010 0.746 0.016 0.753 0.118 0.696 0.068 0.312 0.763 0.576 0.651 0.5964

0.018 0.705 0.004 0.800 0.628 0.522 0.543 0.457 0.191 0.667 0.366 0.6295

0.004 0.705 0.002 0.800 0.236 0.663 0.753 0.457 0.482 0.576 0.846 0.4965

0.245 0.398 0.080 0.287 0.788 0.551 0.249 0.399 0.035 0.182 0.796 0.4965

0.318 0.377 0.053 0.330 0.389 0.623 0.463 0.428 0.016 0.136 0.478 0.6255

0.000 0.807 0.001 0.872 0.053 0.768 0.075 0.312 0.366 0.621 0.272 0.6295

0.021 0.705 0.026 0.706 0.196 0.623 0.075 0.302 0.920 0.545 0.699 0.4625

0.005 0.766 0.011 0.753 0.085 0.768 0.600 0.428 0.615 0.583 0.606 0.4625

0.692 0.480 0.038 0.291 0.024 0.804 0.051 0.283 0.004 0.045 0.897 0.4925

0.129 0.623 0.387 0.587 0.085 0.732 0.325 0.399 0.159 0.318 0.561 0.563

0.556 0.439 0.049 0.314 0.085 0.732 0.173 0.341 0.027 0.182 0.699 0.425

0.042 0.684 0.031 0.729 0.389 0.587 0.850 0.457 0.315 0.629 0.519 0.400

0.014 0.295 0.028 0.263 0.106 0.301 0.137 0.621 0.688 0.447 0.439 0.429

0.004 0.234 0.005 0.211 0.095 0.301 0.011 0.795 1.000 0.492 0.272 0.363

0.001 0.214 0.002 0.196 0.076 0.268 0.116 0.650 0.269 0.364 0.747 0.429

0.010 0.275 0.011 0.192 0.178 0.337 0.116 0.688 0.269 0.356 0.699 0.463

0.042 0.316 0.034 0.287 0.360 0.373 0.346 0.601 0.482 0.402 0.478 0.463

0.078 0.664 0.045 0.706 0.590 0.587 0.325 0.399 0.070 0.758 0.747 0.533

0.065 0.684 0.009 0.777 0.872 0.514 0.216 0.370 0.007 0.939 0.796 0.462

0.005 0.746 0.000 0.872 0.872 0.518 0.438 0.447 0.027 0.803 0.175 0.296

0.194 0.623 0.041 0.709 0.667 0.482 0.160 0.370 0.044 0.765 0.897 0.462

0.087 0.643 0.045 0.729 0.667 0.551 0.042 0.225 0.228 0.712 0.651 0.596

0.061 0.664 0.016 0.753 0.872 0.478 0.107 0.312 0.044 0.811 0.747 0.5251

0.015 0.746 0.001 0.824 0.957 0.482 0.304 0.399 0.009 0.856 0.796 0.5581

0.036 0.705 0.018 0.777 0.518 0.587 0.543 0.428 0.027 0.848 1.000 0.4961

0.005 0.725 0.004 0.824 0.178 0.696 0.137 0.341 0.228 0.674 0.747 0.4621

0.002 0.766 0.005 0.800 0.060 0.732 0.068 0.312 0.688 0.538 0.606 0.4291

0.122 0.357 0.087 0.306 0.590 0.406 0.983 0.514 0.108 0.273 0.138 0.6921

0.028 0.295 0.011 0.211 0.554 0.446 0.173 0.630 0.088 0.265 0.220 0.3671

0.007 0.725 0.038 0.753 0.024 0.804 0.173 0.341 0.366 0.402 0.796 0.5251

0.061 0.664 0.136 0.658 0.132 0.732 0.082 0.312 0.546 0.402 0.519 0.5582

0.001 0.807 0.001 0.872 0.067 0.696 0.062 0.283 0.421 0.583 0.606 0.4292

0.065 0.664 0.118 0.682 0.178 0.659 0.116 0.312 0.920 0.492 0.093 0.6923

0.001 0.193 0.003 0.200 0.027 0.199 0.438 0.592 1.000 0.538 0.561 0.4673

0.015 0.275 0.019 0.291 0.178 0.301 0.390 0.621 0.366 0.364 0.796 0.4964

0.004 0.766 0.041 0.709 0.006 0.841 0.051 0.254 0.108 0.273 0.651 0.42953

0.003 0.254 0.013 0.247 0.031 0.236 0.917 0.476 0.269 0.667 0.519 0.46260

0.000 0.152 0.001 0.196 0.001 0.058 0.126 0.679 0.108 0.712 1.000 0.52962

0.034 0.316 0.136 0.338 0.046 0.236 0.126 0.679 0.421 0.621 0.846 0.49666

0.024 0.275 0.038 0.287 0.162 0.308 0.090 0.708 0.763 0.530 0.561 0.42967

0.001 0.214 0.005 0.223 0.021 0.199 0.390 0.592 0.841 0.447 0.138 0.30079

0.017 0.254 0.038 0.267 0.095 0.304 0.249 0.650 1.000 0.538 0.220 0.36788

indicates data missing or illegible when filed

TABLE B Biomarkers identified in F1CSH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/

P P P P P P (avg

value ROC value ROC value ROC value ROC value ROC value ROC (Da

0.069 0.316 0.101 0.310 0.236 0.341 0.016 0.766 0.841 0.492 0.156 0.33310

0.028 0.705 0.080 0.682 0.076 0.732 0.346 0.399 0.688 0.447 0.302 0.62510

0.018 0.725 0.191 0.634 0.006 0.877 0.116 0.341 0.035 0.182 0.107 0.68811

0.011 0.725 0.053 0.706 0.027 0.804 0.068 0.312 0.228 0.318 0.107 0.69211

0.004 0.766 0.018 0.753 0.024 0.804 0.438 0.428 0.421 0.364 0.033 0.78811

0.030 0.684 0.179 0.658 0.021 0.804 0.917 0.514 0.070 0.273 0.366 0.62912

0.092 0.602 0.367 0.587 0.046 0.768 0.722 0.543 0.132 0.318 0.606 0.52912

0.042 0.684 0.348 0.587 0.008 0.841 0.543 0.601 0.003 0.045 0.897 0.49612

0.021 0.725 0.146 0.634 0.015 0.804 0.722 0.543 0.070 0.273 0.439 0.56212

0.006 0.725 0.016 0.729 0.060 0.732 0.660 0.428 0.763 0.538 1.000 0.50012

0.002 0.766 0.004 0.800 0.060 0.768 0.572 0.399 0.841 0.439 0.949 0.46712

0.018 0.705 0.016 0.729 0.258 0.659 0.691 0.428 0.615 0.583 0.846 0.46712

0.010 0.725 0.013 0.729 0.146 0.696 0.630 0.428 0.421 0.583 0.796 0.49612

0.003 0.766 0.014 0.753 0.027 0.732 0.630 0.457 0.421 0.402 0.699 0.49612

0.001 0.807 0.006 0.800 0.013 0.804 0.630 0.428 0.421 0.447 0.699 0.46212

0.002 0.786 0.007 0.777 0.027 0.804 0.463 0.399 0.763 0.485 0.747 0.49612

0.002 0.766 0.012 0.777 0.018 0.804 0.660 0.476 0.688 0.447 0.272 0.36312

0.000 0.848 0.002 0.800 0.008 0.841 0.368 0.399 0.421 0.356 0.561 0.42912

0.000 0.868 0.001 0.848 0.006 0.877 0.463 0.428 0.841 0.455 0.796 0.4621

0.000 0.827 0.002 0.824 0.006 0.841 0.572 0.428 0.615 0.402 0.949 0.5251

0.001 0.786 0.005 0.800 0.021 0.841 0.173 0.312 0.763 0.538 0.949 0.4921

0.017 0.725 0.045 0.682 0.076 0.732 0.116 0.312 0.920 0.455 0.175 0.6921

0.004 0.234 0.001 0.152 0.419 0.409 0.160 0.650 0.035 0.136 0.245 0.6581

0.008 0.254 0.002 0.172 0.451 0.442 0.148 0.679 0.016 0.136 0.651 0.5581

0.047 0.275 0.013 0.239 0.788 0.442 0.160 0.630 0.027 0.182 0.439 0.4291

0.012 0.746 0.034 0.753 0.067 0.732 0.201 0.370 0.482 0.402 0.478 0.5921

0.042 0.705 0.179 0.658 0.041 0.768 0.304 0.370 0.159 0.311 0.519 0.5961

0.004 0.214 0.031 0.263 0.013 0.159 0.660 0.457 0.269 0.667 0.699 0.4631

0.000 0.827 0.002 0.848 0.015 0.804 0.082 0.341 0.546 0.364 0.053 0.7581

0.092 0.643 0.246 0.587 0.106 0.696 0.516 0.601 0.191 0.273 0.561 0.56220

0.022 0.705 0.045 0.729 0.118 0.732 0.160 0.341 0.482 0.402 0.156 0.69225

0.012 0.725 0.041 0.729 0.053 0.768 0.325 0.399 0.546 0.447 0.093 0.69625

0.001 0.827 0.003 0.800 0.027 0.804 0.046 0.283 0.615 0.447 0.138 0.69226

0.044 0.684 0.058 0.682 0.236 0.659 0.785 0.486 0.421 0.621 0.175 0.32927

0.003 0.786 0.001 0.848 0.196 0.659 0.390 0.428 0.366 0.621 0.121 0.30028

0.010 0.746 0.003 0.800 0.419 0.587 0.850 0.514 0.421 0.667 0.197 0.32929

0.030 0.705 0.011 0.753 0.590 0.627 0.051 0.283 0.027 0.848 0.302 0.65830

0.024 0.725 0.021 0.753 0.282 0.663 0.068 0.312 0.920 0.492 0.519 0.59231

0.001 0.214 0.001 0.176 0.046 0.236 0.148 0.650 0.546 0.447 0.333 0.36739

0.042 0.316 0.019 0.267 0.554 0.409 0.325 0.601 0.044 0.227 0.897 0.49244

0.039 0.316 0.018 0.267 0.554 0.409 0.249 0.630 0.044 0.227 0.897 0.49244

0.019 0.705 0.118 0.658 0.021 0.768 0.068 0.312 0.191 0.318 0.220 0.62550

0.000 0.889 0.000 0.872 0.003 0.877 0.011 0.225 0.269 0.356 0.846 0.52551

0.007 0.725 0.087 0.658 0.005 0.877 0.883 0.534 0.191 0.273 0.220 0.32953

0.008 0.275 0.018 0.243 0.076 0.272 0.630 0.534 0.688 0.538 0.272 0.35858

0.001 0.193 0.003 0.196 0.018 0.199 0.463 0.592 0.546 0.583 0.699 0.42959

0.012 0.295 0.041 0.291 0.053 0.236 0.983 0.534 0.546 0.583 1.000 0.52960

0.004 0.254 0.031 0.287 0.010 0.127 0.346 0.611 0.421 0.629 0.747 0.56762

0.010 0.254 0.013 0.263 0.146 0.344 0.022 0.766 0.763 0.538 0.478 0.45867

0.004 0.234 0.006 0.196 0.085 0.236 0.042 0.708 0.615 0.409 0.478 0.39672

0.018 0.295 0.014 0.243 0.282 0.337 0.046 0.717 0.421 0.356 0.699 0.45874

0.001 0.193 0.002 0.176 0.046 0.272 0.187 0.650 0.546 0.402 0.156 0.30078

0.009 0.254 0.016 0.243 0.106 0.268 0.075 0.737 0.688 0.409 0.272 0.40088

0.013 0.705 0.038 0.706 0.067 0.732 0.090 0.312 0.841 0.492 0.272 0.62996

indicates data missing or illegible when filed

TABLE C Biomarkers identified in F1ISL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/

P P P P P P (av

value ROC value ROC value ROC value ROC value ROC value ROC (D

0.121 0.322 0.061 0.739 0.831 0.466 0.815 0.432 0.062 0.125 0.453 0.429

0.005 0.777 0.055 0.261 0.009 0.920 0.482 0.432 0.126 0.250 0.375 0.598

0.061 0.307 0.010 0.773 0.831 0.523 0.223 0.670 0.042 0.125 0.101 0.313

0.000 0.102 0.001 0.875 0.013 0.125 0.325 0.636 0.396 0.313 0.682 0.429

0.639 0.527 0.778 0.534 0.177 0.693 0.348 0.398 0.126 0.250 0.065 0.705

0.280 0.375 0.024 0.773 0.201 0.636 0.425 0.602 0.062 0.188 0.339 0.670

0.387 0.598 0.639 0.568 0.016 0.864 0.373 0.398 0.042 0.125 0.539 0.589

0.044 0.307 0.019 0.739 0.670 0.409 0.815 0.466 0.308 0.313 0.495 0.393

0.016 0.754 0.101 0.330 0.023 0.807 0.925 0.466 0.126 0.250 0.056 0.732

0.037 0.303 0.015 0.807 0.670 0.409 0.241 0.636 0.234 0.313 0.375 0.598

0.105 0.371 0.028 0.773 0.887 0.466 0.302 0.636 0.089 0.188 0.413 0.563

0.746 0.462 0.325 0.602 0.394 0.636 0.673 0.568 0.234 0.250 1.000 0.491

0.105 0.333 0.083 0.705 0.570 0.432 0.399 0.602 0.497 0.438 0.946 0.455

0.000 0.083 0.000 0.875 0.065 0.239 0.022 0.773 0.089 0.188 0.946 0.5273

0.025 0.258 0.007 0.807 0.776 0.409 0.189 0.636 0.042 0.125 0.413 0.3843

0.044 0.277 0.015 0.773 0.776 0.409 0.205 0.602 0.042 0.125 0.306 0.3573

0.220 0.367 0.039 0.773 0.477 0.636 0.542 0.602 0.042 0.125 0.133 0.3213

0.037 0.299 0.013 0.807 0.722 0.466 0.399 0.602 0.011 0.063 0.133 0.2774

0.084 0.307 0.019 0.773 0.887 0.523 0.122 0.636 0.062 0.188 0.375 0.4114

0.037 0.280 0.017 0.807 0.619 0.409 0.189 0.670 0.308 0.375 0.838 0.4294

0.234 0.636 0.743 0.500 0.065 0.750 0.743 0.432 0.126 0.250 0.029 0.2504

0.589 0.549 0.453 0.602 0.028 0.864 0.606 0.568 0.011 0.063 1.000 0.4914

0.028 0.731 0.015 0.193 0.522 0.636 0.373 0.398 0.308 0.625 0.101 0.6964

0.023 0.258 0.049 0.773 0.136 0.239 0.146 0.705 0.308 0.688 0.453 0.4204

0.011 0.235 0.022 0.773 0.118 0.295 0.325 0.602 0.174 0.250 0.453 0.5985

0.149 0.663 0.189 0.330 0.394 0.636 0.606 0.432 0.610 0.625 0.219 0.6965

0.149 0.330 0.006 0.841 0.177 0.750 0.851 0.466 0.007 0.000 0.116 0.7415

0.105 0.311 0.001 0.875 0.076 0.807 0.281 0.364 0.007 0.000 0.891 0.5365

0.090 0.311 0.002 0.841 0.201 0.750 0.743 0.432 0.007 0.000 0.633 0.5635

0.331 0.402 0.004 0.841 0.013 0.864 0.673 0.500 0.007 0.000 0.375 0.5635

0.639 0.568 0.482 0.602 0.047 0.807 0.425 0.398 0.042 0.125 0.891 0.4916

0.007 0.220 0.002 0.841 0.522 0.409 0.017 0.773 0.062 0.125 0.838 0.4916

0.077 0.348 0.035 0.739 0.776 0.409 0.039 0.739 0.089 0.188 0.585 0.4556

0.025 0.284 0.004 0.841 1.000 0.466 0.061 0.670 0.042 0.125 0.682 0.5636

0.006 0.174 0.002 0.841 0.434 0.409 0.055 0.739 0.089 0.188 0.375 0.5546

0.019 0.261 0.007 0.807 0.619 0.466 0.122 0.705 0.174 0.250 0.306 0.6256

0.012 0.261 0.003 0.841 0.670 0.409 0.049 0.739 0.042 0.125 0.339 0.6256

0.028 0.705 0.024 0.227 0.356 0.636 0.743 0.568 0.497 0.625 0.413 0.3937

0.056 0.708 0.024 0.227 0.722 0.523 0.888 0.534 0.396 0.625 0.495 0.3937

0.195 0.640 0.031 0.193 0.477 0.375 0.223 0.364 0.017 0.938 0.838 0.4919

0.007 0.795 0.001 0.125 0.722 0.523 0.055 0.295 0.174 0.750 0.733 0.4649

0.023 0.727 0.009 0.227 0.619 0.636 0.055 0.261 0.062 0.813 0.495 0.6079

0.009 0.773 0.002 0.159 0.570 0.636 0.002 0.125 0.089 0.813 0.339 0.6079

0.002 0.795 0.002 0.125 0.155 0.693 0.002 0.125 0.734 0.563 0.152 0.6709

0.047 0.686 0.015 0.193 0.831 0.523 0.241 0.364 0.308 0.688 0.076 0.74110

0.007 0.239 0.003 0.841 0.434 0.409 0.005 0.841 0.062 0.188 0.682 0.57113

0.000 0.083 0.000 0.909 0.007 0.091 0.002 0.875 0.734 0.438 0.539 0.42014

0.007 0.239 0.035 0.773 0.033 0.148 0.425 0.398 0.396 0.625 0.891 0.46415

0.097 0.352 0.067 0.739 0.619 0.432 0.888 0.500 0.396 0.375 0.009 0.83923

0.033 0.307 0.031 0.773 0.356 0.375 0.815 0.500 0.610 0.375 0.029 0.77723

0.220 0.394 0.011 0.807 0.155 0.750 0.281 0.636 0.017 0.063 1.000 0.49127

0.207 0.398 0.174 0.670 0.670 0.432 0.815 0.568 0.610 0.375 0.682 0.45534

0.012 0.239 0.004 0.841 0.522 0.375 0.925 0.500 0.174 0.250 0.633 0.4293

0.006 0.197 0.009 0.807 0.136 0.261 0.281 0.602 1.000 0.500 0.838 0.5274

0.004 0.189 0.011 0.807 0.055 0.239 0.673 0.500 0.396 0.375 0.453 0.6074

0.009 0.235 0.007 0.807 0.320 0.352 0.067 0.705 0.174 0.250 1.000 0.4914

0.013 0.258 0.015 0.773 0.227 0.295 0.189 0.670 0.174 0.250 0.339 0.3844

0.009 0.212 0.013 0.807 0.177 0.295 0.189 0.670 0.174 0.250 0.946 0.5274

0.007 0.216 0.013 0.773 0.118 0.239 0.022 0.773 0.174 0.250 0.891 0.5274

0.003 0.216 0.005 0.807 0.102 0.239 0.101 0.670 0.497 0.375 0.585 0.5634

0.044 0.284 0.022 0.739 0.619 0.432 0.075 0.705 0.308 0.313 0.495 0.6345

0.019 0.303 0.004 0.875 0.776 0.432 0.189 0.636 0.234 0.250 0.946 0.5007

0.001 0.125 0.003 0.875 0.023 0.182 0.111 0.705 0.610 0.625 0.838 0.4647

indicates data missing or illegible when filed

TABLE D Biomarkers identified in F1ISH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (

value ROC value ROC value ROC value ROC value ROC value ROC (D

0.017 0.231 0.013 0.193 0.289 0.318 0.093 0.760 0.558 0.413 0.891 0.4641

0.082 0.304 0.061 0.261 0.492 0.409 0.059 0.760 0.661 0.413 0.891 0.4641

0.010 0.255 0.002 0.159 0.574 0.423 0.083 0.695 0.143 0.263 0.048 0.7411

0.013 0.255 0.003 0.159 0.533 0.377 0.059 0.727 0.143 0.263 0.056 0.7411

0.007 0.231 0.002 0.125 0.417 0.377 0.083 0.695 0.306 0.313 0.017 0.7681

0.070 0.304 0.019 0.227 0.851 0.455 0.508 0.571 0.380 0.363 0.020 0.8041

0.290 0.378 0.111 0.295 0.851 0.500 0.959 0.506 0.306 0.313 0.172 0.6341

0.172 0.671 0.673 0.534 0.053 0.773 0.114 0.344 0.107 0.213 0.041 0.7321

0.207 0.647 0.888 0.534 0.034 0.773 0.415 0.442 0.079 0.213 0.453 0.5981

0.116 0.647 0.511 0.602 0.046 0.773 0.878 0.506 0.188 0.313 0.088 0.7051

0.162 0.622 0.743 0.534 0.034 0.818 0.359 0.344 0.143 0.213 0.453 0.6341

0.539 0.427 0.083 0.295 0.236 0.682 0.919 0.503 0.013 0.113 0.585 0.5541

0.290 0.378 0.031 0.227 0.349 0.636 0.610 0.568 0.005 0.013 0.453 0.3931

0.101 0.304 0.024 0.227 1.000 0.500 0.476 0.601 0.143 0.263 0.116 0.2861

0.031 0.745 0.055 0.739 0.170 0.682 0.285 0.633 1.000 0.450 0.172 0.3211

0.034 0.280 0.007 0.159 0.803 0.455 0.093 0.731 0.028 0.113 0.195 0.3211

0.065 0.304 0.019 0.193 0.803 0.468 0.185 0.695 0.107 0.213 0.195 0.3211

0.006 0.231 0.002 0.159 0.349 0.364 0.006 0.825 0.028 0.113 0.133 0.3211

0.003 0.182 0.003 0.159 0.170 0.286 0.008 0.857 0.380 0.313 0.088 0.3211

0.012 0.231 0.028 0.227 0.092 0.273 0.074 0.731 0.661 0.413 0.024 0.2141

0.024 0.255 0.083 0.295 0.070 0.286 0.103 0.727 0.306 0.700 0.008 0.1791

0.034 0.329 0.055 0.295 0.190 0.332 0.103 0.698 1.000 0.500 0.029 0.2141

0.037 0.304 0.083 0.295 0.134 0.286 0.185 0.698 0.770 0.550 0.029 0.2501

0.005 0.231 0.024 0.227 0.034 0.195 0.285 0.633 0.380 0.650 0.152 0.3211

0.006 0.231 0.022 0.227 0.046 0.195 0.203 0.633 0.242 0.700 0.172 0.3211

0.322 0.598 0.925 0.466 0.053 0.818 0.386 0.601 0.028 0.163 0.838 0.563

0.322 0.378 0.035 0.227 0.318 0.682 0.721 0.536 0.079 0.213 0.539 0.571

0.088 0.280 0.024 0.227 0.901 0.500 0.575 0.568 0.057 0.213 0.785 0.571

0.082 0.671 0.122 0.636 0.261 0.682 0.221 0.344 0.884 0.500 0.246 0.634

0.562 0.549 0.963 0.500 0.261 0.636 0.919 0.503 0.188 0.263 0.453 0.393

0.000 0.157 0.004 0.125 0.005 0.105 0.022 0.760 0.770 0.450 0.633 0.563

0.000 0.133 0.001 0.125 0.013 0.150 0.333 0.568 0.770 0.462 0.339 0.321

0.000 0.108 0.001 0.125 0.021 0.195 0.032 0.760 0.306 0.363 0.585 0.393

0.017 0.231 0.031 0.227 0.134 0.318 0.093 0.698 0.380 0.313 0.891 0.491

0.009 0.231 0.017 0.193 0.119 0.273 0.262 0.633 0.306 0.313 0.585 0.420

0.010 0.206 0.028 0.227 0.081 0.227 0.154 0.666 0.661 0.363 0.682 0.455

0.029 0.280 0.044 0.261 0.190 0.332 0.541 0.601 0.464 0.350 0.785 0.455

0.065 0.304 0.019 0.227 0.803 0.455 0.017 0.792 0.040 0.163 0.785 0.455

0.000 0.133 0.000 0.057 0.070 0.255 0.014 0.792 0.558 0.413 0.495 0.420

0.000 0.084 0.000 0.023 0.039 0.209 0.083 0.731 0.770 0.450 0.375 0.357

0.000 0.084 0.000 0.057 0.010 0.164 0.017 0.792 0.661 0.450 0.838 0.491

0.000 0.084 0.000 0.057 0.009 0.164 0.139 0.666 1.000 0.500 0.539 0.420

0.000 0.059 0.000 0.057 0.004 0.118 0.067 0.695 0.380 0.650 0.246 0.348

0.000 0.157 0.004 0.159 0.004 0.073 0.017 0.792 0.306 0.650 0.682 0.491

0.000 0.108 0.002 0.125 0.002 0.073 0.610 0.536 0.143 0.750 0.633 0.455

0.015 0.280 0.011 0.193 0.289 0.332 0.919 0.536 0.380 0.350 0.682 0.4916

0.000 0.157 0.002 0.125 0.021 0.150 0.032 0.760 0.770 0.550 0.682 0.5276

0.004 0.206 0.003 0.125 0.190 0.286 0.059 0.731 0.558 0.400 0.633 0.3936

0.012 0.206 0.015 0.193 0.170 0.318 0.093 0.731 0.464 0.413 0.682 0.4297

0.026 0.329 0.022 0.261 0.318 0.377 0.114 0.698 0.242 0.300 0.495 0.4557

0.001 0.182 0.004 0.159 0.025 0.195 0.093 0.727 0.380 0.363 0.088 0.2867

0.002 0.182 0.003 0.159 0.092 0.273 0.169 0.633 0.380 0.363 0.172 0.3218

0.008 0.231 0.011 0.193 0.134 0.286 0.041 0.760 0.884 0.463 0.891 0.4648

0.026 0.255 0.061 0.261 0.119 0.273 0.067 0.698 1.000 0.462 0.453 0.3939

0.070 0.304 0.019 0.193 0.851 0.468 0.221 0.633 0.242 0.300 0.891 0.46413

indicates data missing or illegible when filed

TABLE E Biomarkers identified in F3CSL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/

P P P P P P (av

value ROC value ROC value ROC value ROC value ROC value ROC (D

0.024 0.316 0.013 0.243 0.419 0.373 0.722 0.563 0.228 0.318 0.161 0.3442

0.203 0.377 0.031 0.287 0.518 0.587 0.414 0.621 0.056 0.227 0.122 0.2712

0.026 0.705 0.191 0.634 0.013 0.804 0.516 0.399 0.159 0.273 0.269 0.3542

0.098 0.664 0.045 0.729 0.747 0.554 0.082 0.312 0.228 0.712 0.768 0.438

0.098 0.643 0.348 0.563 0.060 0.732 0.753 0.534 0.159 0.318 0.161 0.271

0.073 0.664 0.408 0.587 0.021 0.804 0.950 0.514 0.056 0.227 0.417 0.396

0.122 0.643 0.387 0.611 0.076 0.768 0.232 0.370 0.228 0.356 0.417 0.396

0.050 0.664 0.167 0.658 0.067 0.732 0.046 0.283 0.421 0.402 0.376 0.427

0.003 0.746 0.024 0.729 0.013 0.804 0.232 0.399 0.315 0.402 0.883 0.510

0.213 0.398 0.619 0.429 0.085 0.232 0.098 0.679 0.269 0.674 0.768 0.479

0.028 0.684 0.012 0.753 0.518 0.623 0.011 0.196 0.191 0.720 0.338 0.667

0.092 0.664 0.045 0.682 0.706 0.587 0.042 0.312 0.366 0.667 0.210 0.677

0.047 0.643 0.008 0.777 1.000 0.514 0.090 0.283 0.044 0.803 0.825 0.427

0.044 0.684 0.026 0.729 0.484 0.623 0.062 0.312 0.070 0.765 0.376 0.385

0.036 0.684 0.094 0.682 0.095 0.732 0.630 0.457 0.841 0.500 0.269 0.312

0.028 0.275 0.074 0.310 0.085 0.308 0.368 0.592 0.615 0.576 0.338 0.604

0.001 0.214 0.003 0.219 0.015 0.232 0.600 0.543 0.920 0.538 0.555 0.396

0.010 0.254 0.006 0.219 0.282 0.377 0.051 0.708 0.269 0.318 0.461 0.354

0.001 0.193 0.001 0.152 0.106 0.304 0.630 0.437 0.132 0.227 0.768 0.510

0.012 0.316 0.041 0.287 0.053 0.264 0.018 0.737 0.615 0.545 0.238 0.625

0.004 0.234 0.008 0.219 0.076 0.264 0.010 0.795 0.615 0.447 0.269 0.635

0.006 0.254 0.016 0.239 0.060 0.264 0.014 0.766 0.688 0.492 0.461 0.583

0.082 0.336 0.045 0.291 0.628 0.442 0.390 0.592 0.269 0.318 0.027 0.833

0.022 0.295 0.053 0.287 0.095 0.268 0.785 0.514 0.920 0.492 0.658 0.563

0.011 0.275 0.041 0.287 0.046 0.225 0.051 0.737 0.688 0.545 0.461 0.6351

0.004 0.214 0.004 0.215 0.118 0.264 0.022 0.756 0.088 0.273 0.122 0.7501

0.042 0.357 0.094 0.310 0.118 0.301 0.187 0.679 1.000 0.492 0.055 0.7921

0.008 0.254 0.028 0.263 0.041 0.228 0.137 0.679 0.841 0.500 0.376 0.63528

0.007 0.254 0.021 0.263 0.053 0.264 0.173 0.650 0.763 0.538 0.507 0.59429

0.436 0.439 0.191 0.354 0.706 0.558 0.062 0.698 0.615 0.439 0.105 0.27140

0.092 0.357 0.101 0.358 0.360 0.373 0.572 0.543 0.920 0.545 0.768 0.47941

0.015 0.295 0.101 0.314 0.018 0.192 0.325 0.360 0.688 0.545 0.210 0.67756

0.022 0.275 0.074 0.314 0.060 0.232 0.818 0.534 0.841 0.538 0.768 0.52156

0.050 0.316 0.038 0.267 0.419 0.409 0.201 0.650 0.482 0.409 0.077 0.31360

0.057 0.664 0.008 0.781 0.914 0.518 0.267 0.389 0.044 0.765 0.883 0.47978

0.019 0.705 0.016 0.753 0.282 0.627 0.160 0.341 0.615 0.583 0.712 0.43892

0.011 0.295 0.026 0.267 0.085 0.261 0.600 0.563 0.421 0.591 0.376 0.59495

indicates data missing or illegible when filed

TABLE F Biomarkers identified in F3CSH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (

value ROC value ROC value ROC value ROC value ROC value ROC (D

0.050 0.336 0.068 0.287 0.236 0.337 0.042 0.708 0.546 0.409 0.555 0.4371

0.318 0.582 0.262 0.634 0.747 0.554 0.249 0.621 0.763 0.538 0.712 0.4691

0.404 0.602 0.118 0.634 0.518 0.446 0.950 0.486 0.088 0.758 0.941 0.5421

0.022 0.316 0.068 0.310 0.067 0.228 0.173 0.650 0.615 0.591 0.077 0.7601

0.018 0.316 0.013 0.215 0.306 0.341 0.137 0.669 0.366 0.356 0.033 0.8021

0.019 0.295 0.016 0.219 0.282 0.337 0.090 0.698 0.269 0.318 0.005 0.8851

0.010 0.725 0.019 0.753 0.095 0.696 0.098 0.312 0.763 0.583 0.417 0.3852

0.003 0.766 0.018 0.753 0.021 0.804 0.368 0.399 1.000 0.500 0.658 0.4272

0.001 0.786 0.005 0.800 0.021 0.804 0.020 0.225 0.920 0.492 0.883 0.5422

0.022 0.316 0.053 0.287 0.095 0.264 0.216 0.650 0.688 0.545 0.302 0.6352

0.032 0.316 0.087 0.310 0.085 0.268 0.267 0.650 0.315 0.629 0.461 0.5942

0.011 0.254 0.024 0.263 0.095 0.268 0.249 0.630 0.841 0.530 0.658 0.563

0.280 0.602 0.101 0.658 0.788 0.446 0.046 0.254 0.366 0.667 0.606 0.427

0.050 0.664 0.012 0.753 0.872 0.478 0.075 0.283 0.132 0.758 0.507 0.563

0.129 0.623 0.031 0.706 0.872 0.482 0.249 0.341 0.070 0.803 0.507 0.604

0.167 0.602 0.041 0.682 0.788 0.478 0.201 0.341 0.035 0.811 0.376 0.594

0.042 0.316 0.087 0.334 0.132 0.337 0.600 0.534 1.000 0.455 1.000 0.521

0.065 0.377 0.049 0.314 0.451 0.409 0.267 0.621 0.688 0.455 0.185 0.271

0.050 0.377 0.026 0.267 0.554 0.409 0.216 0.650 0.269 0.318 0.185 0.313

0.002 0.214 0.003 0.196 0.095 0.268 0.014 0.737 0.421 0.409 0.461 0.563

0.010 0.746 0.026 0.729 0.067 0.732 0.600 0.428 0.688 0.402 0.825 0.438

0.004 0.766 0.007 0.777 0.085 0.732 0.249 0.341 0.269 0.629 0.376 0.354

0.014 0.295 0.058 0.310 0.041 0.225 0.201 0.621 0.546 0.591 0.338 0.625

0.151 0.602 0.179 0.634 0.389 0.623 0.850 0.514 0.615 0.591 0.941 0.4791

0.021 0.275 0.053 0.291 0.085 0.301 0.173 0.650 0.763 0.500 0.027 0.7921

indicates data missing or illegible when filed

TABLE G Biomarkers identified in F5CSL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (avg

value ROC value ROC value ROC value ROC value ROC value ROC Da 0.3970.540 0.320 0.580 0.836 0.500 0.903 0.493 0.366 0.621 0.000 0.031 2

0.081 0.658 0.166 0.686 0.162 0.708 0.544 0.438 0.841 0.447 0.245 0.3442

0.005 0.246 0.003 0.182 0.254 0.375 0.157 0.681 0.027 0.182 0.086 0.2812

0.034 0.678 0.030 0.712 0.325 0.583 0.039 0.243 0.482 0.621 0.298 0.6252

0.017 0.266 0.102 0.314 0.023 0.167 0.169 0.660 0.228 0.682 0.713 0.4382

0.979 0.462 0.972 0.473 0.917 0.458 0.936 0.521 0.546 0.576 0.066 0.2812

0.015 0.266 0.036 0.235 0.087 0.292 0.008 0.819 0.841 0.538 0.221 0.3752

0.000 0.157 0.000 0.129 0.015 0.208 0.019 0.771 0.615 0.447 0.903 0.5002

0.095 0.344 0.214 0.367 0.147 0.292 0.043 0.736 0.421 0.667 0.713 0.5633

0.053 0.364 0.065 0.341 0.276 0.333 0.075 0.701 0.546 0.447 0.076 0.2813

0.341 0.588 0.320 0.633 0.678 0.542 0.039 0.299 0.482 0.629 0.066 0.7503

0.118 0.384 0.095 0.367 0.534 0.458 0.518 0.590 0.841 0.492 0.008 0.1883

0.009 0.266 0.005 0.208 0.300 0.375 0.146 0.674 0.035 0.182 0.501 0.4063

0.026 0.678 0.065 0.686 0.097 0.708 0.968 0.521 0.841 0.455 0.327 0.6563

0.090 0.325 0.127 0.341 0.276 0.375 0.544 0.549 0.615 0.402 0.462 0.4063

0.427 0.403 0.696 0.447 0.325 0.333 0.467 0.569 0.615 0.591 0.668 0.5633

0.001 0.216 0.025 0.288 0.003 0.125 0.106 0.701 0.159 0.674 0.759 0.5003

0.057 0.678 0.118 0.659 0.147 0.667 0.258 0.382 1.000 0.500 0.624 0.5634

0.011 0.737 0.013 0.765 0.178 0.667 0.006 0.188 0.841 0.538 1.000 0.5314

0.026 0.697 0.055 0.712 0.120 0.750 0.090 0.326 0.688 0.583 0.951 0.4694

0.013 0.737 0.065 0.712 0.029 0.833 0.396 0.410 0.088 0.227 0.951 0.5004

0.017 0.678 0.001 0.818 1.000 0.500 0.312 0.410 0.035 0.803 0.713 0.5314

0.001 0.795 0.001 0.845 0.120 0.750 0.063 0.299 0.159 0.712 0.111 0.6884

0.002 0.776 0.006 0.818 0.049 0.750 0.024 0.243 0.763 0.538 0.391 0.5944

0.032 0.697 0.095 0.686 0.078 0.750 0.196 0.354 0.763 0.409 0.951 0.5004

0.006 0.275 0.000 0.129 0.917 0.458 0.332 0.597 0.007 0.091 0.903 0.5314

0.368 0.560 0.776 0.500 0.029 0.750 0.063 0.271 0.009 0.136 0.501 0.5634

0.017 0.737 0.145 0.659 0.011 0.833 0.052 0.299 0.035 0.182 0.501 0.5944

0.016 0.737 0.060 0.686 0.049 0.750 0.353 0.354 0.132 0.265 0.391 0.5944

0.005 0.737 0.051 0.712 0.010 0.833 0.009 0.215 0.315 0.364 0.358 0.5944

0.090 0.638 0.256 0.633 0.097 0.708 0.716 0.438 0.191 0.318 0.713 0.5634

0.039 0.678 0.110 0.659 0.087 0.708 0.872 0.493 0.615 0.455 0.358 0.6254

0.009 0.737 0.012 0.765 0.147 0.708 0.017 0.243 0.482 0.591 0.426 0.5944

0.023 0.678 0.047 0.712 0.120 0.708 0.312 0.382 0.841 0.500 0.951 0.5004

0.050 0.678 0.019 0.739 0.678 0.583 0.169 0.653 0.421 0.629 0.221 0.6254

0.005 0.776 0.019 0.765 0.034 0.792 0.293 0.382 0.688 0.409 0.270 0.6254

0.014 0.697 0.002 0.792 0.756 0.542 0.599 0.438 0.056 0.803 0.759 0.438

0.021 0.678 0.030 0.712 0.178 0.667 0.396 0.410 0.841 0.538 0.951 0.531

0.004 0.756 0.001 0.818 0.378 0.542 0.157 0.354 0.070 0.765 0.806 0.469

0.001 0.216 0.001 0.182 0.133 0.333 0.903 0.486 0.108 0.273 0.668 0.469

0.013 0.294 0.000 0.129 0.437 0.583 0.628 0.590 0.003 0.045 0.854 0.531

0.050 0.344 0.023 0.261 0.604 0.417 0.029 0.729 0.088 0.265 0.759 0.406

0.000 0.157 0.000 0.102 0.043 0.250 0.026 0.764 0.159 0.318 0.951 0.500

0.001 0.216 0.001 0.208 0.070 0.292 0.002 0.819 0.228 0.318 0.854 0.438

0.001 0.207 0.001 0.182 0.078 0.292 0.075 0.708 0.088 0.273 0.903 0.469

0.000 0.207 0.001 0.155 0.070 0.250 0.069 0.687 0.056 0.227 0.903 0.500

0.003 0.246 0.004 0.155 0.097 0.250 0.090 0.681 0.088 0.227 0.759 0.469

0.000 0.098 0.000 0.102 0.005 0.125 0.106 0.681 0.421 0.356 0.713 0.56369

0.004 0.776 0.076 0.633 0.002 0.917 0.442 0.438 0.108 0.273 0.270 0.59472

0.047 0.658 0.241 0.633 0.029 0.750 0.777 0.493 0.269 0.356 0.391 0.5947

0.004 0.207 0.013 0.208 0.038 0.208 0.063 0.715 0.688 0.538 0.951 0.4697

0.032 0.314 0.118 0.314 0.055 0.250 0.312 0.597 0.421 0.621 0.582 0.4068

0.010 0.286 0.009 0.208 0.213 0.333 0.052 0.729 0.269 0.364 0.426 0.5948

0.001 0.216 0.000 0.129 0.195 0.333 0.011 0.757 0.007 0.091 0.951 0.5318

0.023 0.325 0.025 0.314 0.233 0.375 0.014 0.764 0.688 0.455 0.759 0.5318

0.005 0.255 0.011 0.261 0.078 0.292 0.026 0.757 0.482 0.409 0.358 0.6259

0.013 0.266 0.028 0.288 0.097 0.250 0.196 0.660 0.688 0.409 0.668 0.4389

0.081 0.678 0.065 0.712 0.468 0.583 0.374 0.410 0.366 0.621 0.951 0.50010

0.003 0.216 0.005 0.208 0.078 0.292 0.026 0.736 0.366 0.402 0.806 0.50013

0.005 0.286 0.021 0.261 0.038 0.208 0.012 0.792 0.841 0.530 0.501 0.5631

0.204 0.364 0.859 0.473 0.026 0.208 0.052 0.708 0.016 0.856 0.854 0.53117

0.002 0.196 0.002 0.182 0.097 0.292 0.075 0.708 0.546 0.394 0.582 0.59421

0.001 0.196 0.002 0.155 0.062 0.292 0.019 0.792 0.366 0.356 1.000 0.46922

0.002 0.196 0.002 0.155 0.133 0.292 0.010 0.764 0.315 0.318 0.713 0.43822

0.001 0.196 0.005 0.208 0.017 0.208 0.032 0.729 0.763 0.530 0.426 0.59425

0.013 0.286 0.060 0.288 0.034 0.167 0.015 0.785 0.228 0.674 0.759 0.50028

0.019 0.275 0.095 0.288 0.029 0.167 0.036 0.736 0.159 0.720 0.540 0.56328

0.021 0.286 0.028 0.261 0.195 0.333 0.293 0.632 0.763 0.447 0.327 0.37533

0.004 0.235 0.004 0.182 0.147 0.292 0.011 0.764 0.688 0.402 0.178 0.31343

0.003 0.235 0.002 0.182 0.213 0.333 0.011 0.806 0.228 0.311 0.111 0.28143

0.000 0.176 0.000 0.102 0.038 0.250 0.005 0.813 0.315 0.356 0.624 0.43844

0.000 0.137 0.000 0.076 0.020 0.208 0.005 0.833 0.366 0.402 0.582 0.46945

0.000 0.176 0.000 0.129 0.070 0.250 0.008 0.813 0.920 0.447 0.159 0.28146

0.011 0.246 0.013 0.208 0.178 0.292 0.024 0.764 0.366 0.318 0.066 0.28147

0.204 0.392 0.102 0.367 0.917 0.458 0.069 0.729 0.191 0.318 0.111 0.31354

0.026 0.314 0.036 0.288 0.195 0.333 0.048 0.729 0.920 0.492 1.000 0.46955

0.050 0.333 0.009 0.235 1.000 0.500 0.043 0.757 0.132 0.311 0.501 0.40657

0.000 0.137 0.000 0.129 0.015 0.208 0.001 0.889 0.763 0.492 0.903 0.56359

0.000 0.196 0.000 0.155 0.070 0.250 0.002 0.861 0.615 0.447 0.624 0.43859

0.001 0.235 0.002 0.208 0.078 0.292 0.003 0.833 0.688 0.447 0.806 0.43860

0.004 0.216 0.004 0.182 0.133 0.333 0.000 0.889 0.688 0.447 0.713 0.46960

0.002 0.235 0.006 0.208 0.049 0.250 0.003 0.833 0.841 0.492 0.462 0.37561

0.001 0.216 0.007 0.235 0.020 0.208 0.002 0.861 0.688 0.538 0.391 0.37562

0.001 0.188 0.001 0.155 0.062 0.250 0.012 0.792 0.366 0.356 0.759 0.4386

0.001 0.235 0.001 0.182 0.097 0.292 0.001 0.889 0.615 0.492 0.298 0.3757

0.001 0.196 0.001 0.155 0.087 0.292 0.001 0.868 0.763 0.492 0.198 0.3137

0.000 0.157 0.000 0.155 0.038 0.250 0.001 0.840 0.482 0.364 0.951 0.4697

0.266 0.412 0.887 0.473 0.049 0.250 0.036 0.736 0.132 0.765 0.759 0.5318

0.002 0.188 0.001 0.129 0.162 0.333 0.039 0.736 0.088 0.227 0.759 0.4698

0.000 0.196 0.001 0.155 0.049 0.250 0.043 0.729 0.269 0.318 0.854 0.4699

indicates data missing or illegible when filed

TABLE H Biomarkers identified in F5CSH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (a

value ROC value ROC value ROC value ROC value ROC value ROC Da 0.0210.316 0.028 0.263 0.178 0.301 0.201 0.688 0.482 0.409 0.333 0.363 1

0.047 0.316 0.087 0.330 0.162 0.301 0.057 0.708 0.920 0.545 0.651 0.5631

0.036 0.684 0.053 0.706 0.196 0.659 0.600 0.447 0.315 0.674 0.093 0.6961

0.022 0.725 0.028 0.706 0.196 0.659 0.818 0.476 0.228 0.674 0.053 0.7581

0.030 0.705 0.038 0.729 0.216 0.696 0.285 0.370 0.421 0.629 0.561 0.5631

0.009 0.746 0.013 0.777 0.132 0.696 0.216 0.341 0.421 0.583 1.000 0.4631

0.001 0.807 0.000 0.848 0.132 0.696 0.057 0.283 0.016 0.848 0.897 0.4961

0.013 0.684 0.038 0.706 0.067 0.696 0.038 0.283 0.763 0.447 0.747 0.5291

0.018 0.705 0.008 0.753 0.419 0.623 0.216 0.312 0.070 0.803 0.796 0.5251

0.008 0.725 0.005 0.777 0.282 0.627 0.572 0.399 0.191 0.712 0.747 0.4331

0.005 0.746 0.011 0.777 0.067 0.732 0.075 0.283 0.841 0.538 0.846 0.5581

0.006 0.746 0.002 0.824 0.360 0.623 0.187 0.341 0.088 0.712 0.846 0.5251

0.001 0.807 0.001 0.848 0.053 0.768 0.051 0.283 0.841 0.492 0.651 0.5961

0.017 0.746 0.021 0.753 0.178 0.696 0.098 0.283 0.366 0.629 0.699 0.5291

0.000 0.848 0.000 0.919 0.306 0.591 0.267 0.389 0.108 0.758 0.651 0.5921

0.003 0.746 0.002 0.824 0.196 0.696 0.042 0.254 0.315 0.667 0.846 0.5251

0.003 0.766 0.001 0.824 0.216 0.696 0.042 0.225 0.228 0.674 0.846 0.4631

0.000 0.848 0.000 0.872 0.041 0.768 0.126 0.370 0.132 0.712 0.519 0.4291

0.050 0.316 0.019 0.259 0.667 0.409 0.046 0.727 0.088 0.265 0.366 0.3961

0.002 0.214 0.003 0.172 0.060 0.264 0.107 0.650 0.366 0.356 0.245 0.6581

0.003 0.254 0.005 0.215 0.095 0.301 0.034 0.717 0.421 0.409 0.796 0.4961

0.030 0.295 0.026 0.259 0.306 0.370 0.098 0.679 0.070 0.273 0.651 0.5581

0.104 0.643 0.026 0.729 0.957 0.514 0.722 0.428 0.009 0.894 0.197 0.6581

0.575 0.459 0.429 0.571 0.024 0.192 0.850 0.457 0.027 0.856 0.561 0.5921

0.004 0.786 0.003 0.824 0.196 0.659 0.034 0.283 0.108 0.720 0.606 0.4292

0.021 0.254 0.008 0.235 0.518 0.409 0.267 0.630 0.108 0.265 0.478 0.5922

0.039 0.316 0.118 0.354 0.076 0.228 0.057 0.708 0.269 0.674 0.897 0.5292

0.011 0.254 0.008 0.247 0.236 0.377 0.028 0.756 0.615 0.402 0.245 0.3674

0.000 0.152 0.000 0.105 0.041 0.232 0.005 0.795 0.763 0.447 0.699 0.5004

0.305 0.582 0.408 0.587 0.419 0.623 0.346 0.370 0.763 0.492 0.197 0.6634

0.007 0.746 0.013 0.753 0.095 0.732 0.917 0.486 0.482 0.583 0.197 0.329

0.001 0.786 0.003 0.800 0.053 0.768 0.489 0.428 0.841 0.538 0.366 0.396

0.019 0.275 0.019 0.263 0.236 0.337 0.016 0.766 0.421 0.364 0.401 0.396

0.018 0.275 0.018 0.263 0.236 0.301 0.020 0.737 0.482 0.409 0.333 0.363

0.001 0.193 0.000 0.148 0.106 0.268 0.022 0.795 0.421 0.402 0.796 0.429

0.003 0.234 0.005 0.196 0.085 0.264 0.090 0.708 0.421 0.409 0.699 0.563

0.010 0.275 0.068 0.306 0.015 0.196 0.187 0.659 0.159 0.674 0.081 0.729

0.213 0.357 0.985 0.540 0.015 0.159 0.630 0.572 0.002 0.939 0.081 0.725

0.001 0.173 0.001 0.144 0.118 0.264 0.020 0.766 0.088 0.265 0.699 0.429

0.001 0.193 0.001 0.144 0.178 0.337 0.098 0.737 0.191 0.356 0.949 0.462

0.001 0.193 0.001 0.148 0.067 0.232 0.137 0.659 0.132 0.318 0.366 0.3631

0.044 0.336 0.028 0.271 0.451 0.409 0.148 0.630 0.546 0.455 0.561 0.4291

0.003 0.254 0.004 0.172 0.106 0.301 0.002 0.843 0.482 0.364 0.949 0.4581

0.012 0.275 0.013 0.239 0.196 0.337 0.034 0.727 0.228 0.318 1.000 0.4921

0.000 0.111 0.000 0.101 0.011 0.163 0.014 0.795 0.615 0.455 0.846 0.4581

0.000 0.173 0.001 0.125 0.031 0.196 0.126 0.659 0.191 0.273 0.333 0.3631

indicates data missing or illegible when filed

TABLE I Biomarkers identified in F5ISL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (avg

value ROC value ROC value ROC value ROC value ROC value ROC Da 0.2040.392 0.831 0.500 0.029 0.250 0.808 0.521 0.056 0.765 0.582 0.438 2

0.397 0.442 0.915 0.473 0.133 0.333 0.032 0.736 0.088 0.712 0.391 0.4062

0.112 0.638 0.271 0.606 0.133 0.708 0.012 0.236 0.366 0.356 0.806 0.4692

0.195 0.353 0.189 0.314 0.534 0.458 1.000 0.521 0.688 0.447 0.462 0.4062

0.101 0.384 0.095 0.341 0.437 0.417 0.716 0.451 0.763 0.492 0.501 0.4382

0.223 0.353 0.303 0.367 0.378 0.375 0.135 0.674 0.615 0.409 0.003 0.1252

0.177 0.373 0.189 0.367 0.468 0.417 0.746 0.521 1.000 0.538 0.086 0.3442

0.037 0.314 0.166 0.367 0.038 0.250 0.396 0.576 0.920 0.500 0.221 0.3752

0.223 0.373 0.241 0.394 0.500 0.417 0.210 0.646 0.546 0.447 0.540 0.4382

0.341 0.599 0.286 0.633 0.756 0.542 0.968 0.486 0.763 0.538 0.806 0.5002

0.791 0.529 0.972 0.500 0.568 0.583 0.396 0.458 0.421 0.447 0.017 0.2192

0.278 0.403 0.166 0.341 0.917 0.458 0.032 0.736 0.269 0.318 0.462 0.5942

0.001 0.188 0.001 0.129 0.078 0.292 0.106 0.701 0.056 0.273 0.462 0.4062

0.090 0.353 0.065 0.288 0.534 0.417 0.777 0.479 0.482 0.409 0.358 0.3752

0.578 0.549 0.619 0.527 0.717 0.542 0.808 0.542 0.841 0.485 0.032 0.2192

0.234 0.373 0.189 0.394 0.678 0.417 0.005 0.819 0.546 0.409 0.298 0.3752

0.214 0.619 0.414 0.606 0.213 0.625 0.106 0.299 0.546 0.455 0.903 0.5632

0.004 0.235 0.011 0.261 0.062 0.292 0.746 0.486 0.920 0.492 0.462 0.4062

0.459 0.423 0.227 0.367 0.756 0.500 0.275 0.674 0.421 0.364 0.327 0.3752

0.118 0.373 0.060 0.314 0.756 0.500 0.135 0.326 0.763 0.485 0.540 0.4382

0.354 0.580 0.943 0.500 0.055 0.750 0.374 0.424 0.421 0.409 0.142 0.3132

0.475 0.423 0.394 0.394 0.876 0.458 0.746 0.514 0.763 0.576 0.462 0.4062

0.234 0.619 1.000 0.473 0.020 0.833 0.419 0.431 0.269 0.364 0.501 0.4063

0.874 0.521 0.722 0.447 0.407 0.583 0.075 0.701 0.269 0.318 0.032 0.2193

0.000 0.176 0.001 0.182 0.029 0.208 0.048 0.708 0.920 0.492 0.298 0.3753

0.412 0.423 0.776 0.447 0.233 0.292 0.571 0.458 0.421 0.583 0.327 0.3753

0.214 0.373 0.166 0.367 0.678 0.458 0.872 0.486 0.366 0.364 0.391 0.6563

0.047 0.294 0.011 0.235 0.876 0.500 0.442 0.542 0.056 0.227 0.903 0.4693

0.053 0.658 0.201 0.606 0.055 0.750 0.599 0.465 0.366 0.364 0.007 0.1563

0.068 0.353 0.136 0.394 0.162 0.333 0.442 0.590 1.000 0.500 1.000 0.5003

0.634 0.442 0.776 0.447 0.604 0.417 0.968 0.493 0.546 0.538 0.462 0.4063

0.177 0.384 0.088 0.314 0.876 0.458 0.021 0.757 0.763 0.492 0.624 0.4693

0.081 0.333 0.177 0.341 0.147 0.292 0.135 0.674 0.366 0.667 0.624 0.5943

0.037 0.305 0.055 0.261 0.195 0.333 0.872 0.514 0.315 0.364 0.624 0.4383

0.525 0.521 0.594 0.527 0.641 0.500 0.936 0.521 0.841 0.455 0.624 0.438

0.002 0.776 0.016 0.739 0.010 0.833 0.048 0.271 0.366 0.364 0.540 0.594

0.001 0.795 0.012 0.739 0.006 0.833 0.057 0.271 0.763 0.591 0.713 0.469

0.302 0.599 0.456 0.553 0.351 0.625 0.057 0.299 0.615 0.409 0.668 0.438

0.131 0.619 0.356 0.633 0.108 0.667 0.657 0.438 0.315 0.318 0.426 0.625

0.076 0.638 0.177 0.633 0.133 0.708 0.571 0.438 0.688 0.455 0.759 0.563

0.010 0.737 0.060 0.712 0.023 0.792 0.052 0.271 0.159 0.273 0.951 0.500

0.397 0.580 0.155 0.633 0.678 0.417 0.374 0.410 0.070 0.765 0.076 0.750

0.068 0.658 0.110 0.659 0.213 0.625 0.135 0.326 0.920 0.447 0.159 0.656

0.006 0.737 0.008 0.765 0.133 0.708 0.010 0.215 0.366 0.674 0.098 0.719

0.016 0.678 0.088 0.686 0.026 0.792 0.009 0.215 0.228 0.318 0.221 0.625

0.341 0.580 0.570 0.580 0.300 0.667 0.777 0.493 0.315 0.364 0.759 0.5634

0.060 0.658 0.110 0.633 0.178 0.625 0.396 0.382 0.763 0.492 0.759 0.4694

0.050 0.678 0.047 0.686 0.351 0.625 0.903 0.493 0.841 0.538 0.426 0.3755

0.001 0.176 0.003 0.208 0.026 0.208 0.048 0.736 0.421 0.409 0.391 0.3758

0.112 0.344 0.776 0.473 0.007 0.167 0.069 0.687 0.005 0.902 0.178 0.3131

0.008 0.286 0.055 0.261 0.017 0.167 0.069 0.687 0.088 0.758 0.903 0.46933

0.010 0.266 0.043 0.261 0.038 0.208 0.293 0.576 0.159 0.667 0.540 0.40634

0.021 0.305 0.082 0.367 0.049 0.208 0.312 0.604 0.159 0.674 0.391 0.37534

0.028 0.294 0.214 0.341 0.013 0.167 0.419 0.597 0.056 0.758 0.462 0.40635

0.003 0.246 0.009 0.208 0.049 0.208 0.146 0.604 0.841 0.530 1.000 0.46936

0.578 0.451 0.831 0.473 0.437 0.417 0.196 0.674 0.546 0.576 0.624 0.43842

0.015 0.266 0.110 0.314 0.015 0.167 0.057 0.715 0.070 0.803 0.668 0.53142

0.050 0.333 0.414 0.420 0.008 0.167 0.125 0.674 0.044 0.811 0.582 0.43843

0.042 0.325 0.051 0.314 0.254 0.333 0.275 0.632 0.920 0.492 0.624 0.43844

0.003 0.227 0.036 0.288 0.004 0.125 0.057 0.708 0.159 0.720 0.391 0.43844

0.034 0.325 0.320 0.447 0.007 0.167 0.012 0.764 0.035 0.856 0.582 0.43845

0.003 0.207 0.009 0.208 0.049 0.208 0.015 0.764 0.615 0.583 1.000 0.53146

0.015 0.266 0.070 0.341 0.034 0.208 0.057 0.674 0.228 0.674 0.903 0.50046

0.010 0.235 0.047 0.261 0.029 0.208 0.024 0.743 1.000 0.492 0.540 0.59447

0.234 0.384 0.915 0.527 0.013 0.208 0.936 0.458 0.027 0.856 0.854 0.46958

0.006 0.266 0.028 0.261 0.029 0.250 0.090 0.660 0.159 0.674 0.759 0.43866

indicates data missing or illegible when filed

TABLE J Biomarkers identified in F5ISH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/Z P P P P P P (

value ROC value ROC value ROC value ROC value ROC value ROC D

0.007 0.258 0.018 0.252 0.065 0.303 0.302 0.636 1.000 0.455 0.133 0.7051

0.005 0.258 0.013 0.227 0.057 0.303 0.133 0.739 0.688 0.409 0.219 0.6611

0.019 0.295 0.051 0.295 0.073 0.303 0.061 0.705 0.763 0.455 0.152 0.6961

0.009 0.258 0.027 0.271 0.057 0.265 0.260 0.670 0.841 0.492 0.585 0.5891

0.008 0.277 0.018 0.271 0.083 0.273 0.260 0.670 0.920 0.447 0.838 0.5541

0.012 0.258 0.027 0.252 0.083 0.303 0.425 0.602 0.763 0.447 0.585 0.5981

0.014 0.262 0.043 0.277 0.057 0.273 0.075 0.739 0.920 0.492 0.891 0.527

0.007 0.262 0.022 0.252 0.044 0.235 0.044 0.773 0.763 0.538 0.785 0.563

0.005 0.243 0.016 0.252 0.044 0.197 0.083 0.705 0.920 0.455 0.785 0.562

0.020 0.299 0.047 0.320 0.093 0.273 0.241 0.670 1.000 0.485 0.172 0.705

0.044 0.318 0.093 0.302 0.131 0.311 0.133 0.705 0.920 0.500 0.246 0.670

0.047 0.337 0.136 0.326 0.083 0.235 0.159 0.670 0.615 0.538 0.495 0.598

0.089 0.337 0.340 0.401 0.050 0.235 0.122 0.670 0.228 0.629 0.172 0.670

0.066 0.337 0.208 0.351 0.073 0.235 0.122 0.705 0.482 0.583 0.946 0.491

0.054 0.322 0.117 0.357 0.131 0.280 0.039 0.739 0.763 0.538 0.891 0.527

0.044 0.303 0.158 0.333 0.057 0.280 0.302 0.602 0.482 0.629 0.838 0.491

0.095 0.360 0.321 0.426 0.065 0.242 0.035 0.739 0.366 0.629 0.495 0.429

0.100 0.651 0.086 0.692 0.467 0.576 0.963 0.500 0.421 0.583 0.246 0.357

0.034 0.722 0.127 0.667 0.050 0.758 0.061 0.295 0.546 0.409 0.495 0.4201

0.149 0.610 0.340 0.568 0.145 0.682 0.888 0.432 0.688 0.447 0.785 0.4911

0.100 0.647 0.181 0.643 0.198 0.682 0.302 0.364 0.841 0.492 0.946 0.5001

0.100 0.666 0.321 0.593 0.073 0.720 0.453 0.398 0.159 0.265 0.946 0.5001

0.001 0.797 0.004 0.791 0.038 0.795 0.067 0.261 0.688 0.538 0.785 0.5631

0.017 0.703 0.039 0.692 0.093 0.682 0.189 0.364 0.763 0.455 0.539 0.5981

0.002 0.797 0.003 0.816 0.093 0.720 0.044 0.227 0.366 0.629 0.785 0.5361

0.062 0.684 0.222 0.618 0.057 0.795 0.281 0.398 0.269 0.273 0.339 0.6341

0.003 0.797 0.016 0.742 0.019 0.795 0.091 0.295 0.421 0.356 0.838 0.5271

0.066 0.684 0.158 0.643 0.117 0.720 0.260 0.364 0.315 0.356 0.682 0.4551

0.017 0.703 0.043 0.717 0.083 0.720 0.101 0.295 0.482 0.356 0.733 0.4551

0.017 0.741 0.079 0.667 0.033 0.795 0.101 0.330 0.191 0.311 0.275 0.6701

0.004 0.778 0.015 0.742 0.033 0.795 0.189 0.330 0.615 0.447 0.682 0.5631

0.022 0.703 0.027 0.717 0.198 0.644 0.001 0.125 0.228 0.682 0.539 0.5981

0.002 0.778 0.002 0.816 0.104 0.720 0.091 0.261 0.191 0.667 0.891 0.5271

0.002 0.797 0.012 0.767 0.016 0.795 0.035 0.261 0.615 0.409 0.413 0.6341

0.023 0.722 0.073 0.667 0.065 0.758 0.159 0.295 0.615 0.409 0.682 0.5631

0.010 0.741 0.008 0.767 0.218 0.644 0.061 0.295 0.482 0.621 0.682 0.5631

0.009 0.741 0.020 0.717 0.083 0.720 0.189 0.330 0.688 0.402 0.891 0.4911

0.019 0.703 0.032 0.717 0.131 0.720 0.111 0.330 0.920 0.492 0.785 0.5631

0.010 0.722 0.020 0.742 0.093 0.720 0.061 0.261 0.421 0.636 0.946 0.5631

0.089 0.666 0.236 0.618 0.104 0.758 0.281 0.364 0.228 0.318 0.585 0.5981

0.004 0.778 0.073 0.643 0.002 0.909 0.044 0.295 0.035 0.227 0.733 0.5631

0.047 0.684 0.194 0.593 0.044 0.795 0.007 0.159 0.482 0.409 0.585 0.4551

0.079 0.666 0.208 0.618 0.104 0.720 0.325 0.398 0.688 0.455 0.838 0.5271

0.054 0.647 0.169 0.593 0.073 0.720 0.022 0.261 0.269 0.364 1.000 0.5271

0.058 0.666 0.222 0.618 0.050 0.795 0.044 0.261 0.269 0.318 0.785 0.5631

0.003 0.759 0.032 0.742 0.006 0.833 0.159 0.364 0.269 0.318 0.495 0.4551

0.002 0.797 0.024 0.742 0.005 0.871 0.031 0.261 0.366 0.409 0.585 0.4201

0.047 0.684 0.136 0.643 0.083 0.758 0.075 0.261 0.920 0.455 0.585 0.5631

0.149 0.651 0.731 0.525 0.019 0.795 0.373 0.398 0.108 0.227 0.306 0.5981

0.036 0.726 0.252 0.599 0.014 0.833 0.425 0.432 0.315 0.318 0.306 0.4201

0.193 0.618 0.760 0.531 0.033 0.795 0.111 0.330 0.108 0.273 0.946 0.4911

0.001 0.820 0.006 0.773 0.006 0.871 1.000 0.534 0.482 0.591 0.495 0.5631

0.119 0.632 0.647 0.574 0.016 0.833 0.111 0.330 0.035 0.136 0.246 0.6341

0.066 0.674 0.302 0.605 0.033 0.795 0.174 0.364 0.228 0.318 0.275 0.6341

0.036 0.689 0.194 0.624 0.025 0.758 0.122 0.295 0.088 0.227 0.733 0.5361

0.066 0.670 0.117 0.674 0.179 0.682 0.174 0.364 0.920 0.500 0.733 0.4911

0.223 0.614 0.194 0.667 0.614 0.568 0.888 0.568 0.615 0.545 0.891 0.5271

0.380 0.595 0.703 0.525 0.240 0.644 0.453 0.398 0.191 0.318 0.453 0.5891

0.119 0.360 0.032 0.283 0.955 0.538 0.281 0.636 0.088 0.265 0.539 0.3841

0.039 0.303 0.027 0.258 0.401 0.386 0.348 0.602 0.088 0.227 0.172 0.705

0.012 0.281 0.027 0.277 0.083 0.273 0.260 0.602 0.615 0.409 0.682 0.455

0.019 0.262 0.009 0.227 0.401 0.386 0.302 0.602 0.088 0.273 0.585 0.420

0.799 0.524 0.380 0.407 0.073 0.758 0.453 0.432 0.044 0.182 0.785 0.491

0.005 0.229 0.007 0.209 0.104 0.311 0.039 0.739 0.546 0.409 0.152 0.321

0.023 0.285 0.024 0.233 0.240 0.348 0.159 0.670 0.546 0.402 0.076 0.286

0.001 0.176 0.002 0.165 0.044 0.242 0.009 0.807 0.546 0.409 0.041 0.250

0.023 0.266 0.027 0.233 0.218 0.348 0.639 0.568 0.421 0.364 0.048 0.214

0.003 0.243 0.009 0.233 0.044 0.197 0.348 0.602 0.841 0.500 0.012 0.179

0.034 0.707 0.101 0.649 0.073 0.758 0.028 0.261 0.159 0.311 0.838 0.563

0.022 0.703 0.127 0.692 0.022 0.833 0.511 0.466 0.088 0.273 0.539 0.5634

0.001 0.816 0.004 0.816 0.014 0.795 0.049 0.227 0.763 0.500 0.339 0.6345

0.004 0.759 0.005 0.791 0.093 0.758 0.049 0.295 0.615 0.576 0.838 0.5275

0.002 0.759 0.002 0.791 0.117 0.720 0.044 0.261 0.366 0.629 0.946 0.4915

0.000 0.853 0.002 0.816 0.007 0.871 0.055 0.261 0.920 0.447 0.733 0.5635

0.001 0.816 0.003 0.816 0.029 0.795 0.146 0.330 0.482 0.629 0.838 0.5275

0.025 0.722 0.079 0.667 0.065 0.720 0.111 0.364 0.615 0.455 0.946 0.4915

0.133 0.666 0.359 0.593 0.104 0.720 0.159 0.330 0.228 0.356 0.891 0.5365

0.044 0.707 0.285 0.599 0.016 0.833 0.542 0.364 0.228 0.318 0.152 0.6345

0.023 0.703 0.029 0.717 0.198 0.682 0.673 0.500 0.688 0.538 0.495 0.4205

0.017 0.281 0.051 0.295 0.065 0.242 0.302 0.636 0.763 0.530 0.539 0.3846

0.054 0.318 0.093 0.326 0.179 0.348 0.482 0.568 0.763 0.538 0.101 0.2867

0.193 0.389 0.147 0.326 0.654 0.455 0.133 0.705 0.546 0.402 1.000 0.4558

0.223 0.614 0.703 0.525 0.065 0.758 0.453 0.568 0.044 0.182 0.219 0.3488

0.106 0.647 0.359 0.593 0.065 0.720 0.373 0.568 0.228 0.318 0.375 0.37511

0.000 0.154 0.002 0.153 0.007 0.129 0.017 0.841 0.841 0.538 0.785 0.48213

indicates data missing or illegible when filed

TABLE K Biomarkers identified in F6CSL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/

P P P P P P (av

value ROC value ROC value ROC value ROC value ROC value ROC D

0.132 0.668 0.127 0.667 0.492 0.591 0.459 0.404 0.777 0.564 0.682 0.4642

0.359 0.568 0.541 0.568 0.349 0.636 0.296 0.417 0.777 0.509 0.682 0.4292

0.007 0.733 0.008 0.773 0.170 0.727 0.037 0.265 0.336 0.673 0.682 0.5272

0.036 0.668 0.047 0.717 0.236 0.682 0.514 0.386 0.610 0.564 0.306 0.6342

0.020 0.688 0.067 0.692 0.053 0.727 0.041 0.295 0.777 0.455 0.306 0.6702

0.156 0.628 0.127 0.643 0.618 0.591 0.828 0.477 0.282 0.655 0.339 0.625

0.033 0.707 0.029 0.717 0.349 0.636 0.361 0.386 0.193 0.673 0.633 0.429

0.002 0.222 0.005 0.209 0.061 0.227 0.192 0.720 0.777 0.509 0.585 0.420

0.287 0.588 0.236 0.618 0.755 0.545 0.695 0.447 0.865 0.509 0.785 0.527

0.051 0.707 0.024 0.767 0.662 0.545 0.164 0.326 0.157 0.727 0.306 0.598

0.005 0.773 0.009 0.773 0.092 0.682 0.338 0.386 0.462 0.618 0.413 0.563

0.010 0.727 0.039 0.717 0.039 0.818 0.056 0.295 0.692 0.564 0.733 0.420

0.001 0.827 0.002 0.816 0.029 0.818 0.013 0.235 0.193 0.673 0.946 0.527

0.002 0.807 0.001 0.890 0.318 0.636 0.019 0.265 0.126 0.764 0.838 0.527

0.033 0.693 0.002 0.816 0.618 0.455 0.408 0.629 0.079 0.764 0.306 0.357

0.007 0.767 0.001 0.866 0.950 0.500 0.542 0.447 0.062 0.818 0.246 0.321

0.012 0.747 0.016 0.767 0.170 0.727 0.024 0.235 0.282 0.673 0.495 0.429

0.036 0.688 0.047 0.692 0.236 0.682 0.050 0.295 0.533 0.618 0.891 0.491

0.001 0.787 0.001 0.866 0.261 0.636 0.061 0.265 0.027 0.873 0.785 0.527

0.002 0.787 0.000 0.866 0.574 0.636 0.128 0.326 0.011 0.927 0.785 0.429

0.051 0.315 0.027 0.277 0.618 0.455 0.486 0.616 0.396 0.345 0.785 0.429

0.018 0.281 0.047 0.302 0.081 0.241 0.177 0.677 0.533 0.564 0.306 0.661

0.081 0.688 0.018 0.791 0.851 0.455 0.632 0.447 0.011 0.873 0.133 0.3484

0.261 0.628 0.043 0.742 0.349 0.318 0.338 0.356 0.011 0.873 0.585 0.3935

0.000 0.182 0.000 0.134 0.034 0.195 0.045 0.720 0.157 0.291 0.838 0.4556

0.001 0.202 0.001 0.159 0.081 0.227 0.030 0.720 0.157 0.291 0.339 0.3576

0.001 0.202 0.002 0.134 0.081 0.273 0.117 0.659 0.100 0.182 0.785 0.4556

0.001 0.176 0.001 0.128 0.081 0.227 0.045 0.720 0.020 0.127 0.453 0.3846

0.001 0.182 0.001 0.134 0.070 0.227 0.068 0.689 0.193 0.291 0.733 0.4206

0.044 0.301 0.029 0.258 0.492 0.377 0.017 0.750 0.126 0.273 0.453 0.3846

0.287 0.395 0.147 0.376 0.901 0.545 0.107 0.689 0.004 0.018 0.306 0.3937

0.031 0.713 0.016 0.742 0.533 0.605 0.602 0.434 0.336 0.655 0.838 0.4918

0.013 0.281 0.061 0.308 0.029 0.195 0.663 0.568 0.193 0.673 0.020 0.7688

0.005 0.247 0.015 0.283 0.061 0.241 0.207 0.646 0.955 0.509 0.133 0.7058

0.001 0.182 0.002 0.202 0.034 0.195 0.107 0.689 0.336 0.345 0.539 0.5898

0.001 0.182 0.001 0.134 0.119 0.286 0.030 0.720 0.126 0.236 0.413 0.6258

0.002 0.216 0.002 0.178 0.170 0.318 0.008 0.811 0.100 0.236 0.633 0.5898

0.009 0.256 0.013 0.252 0.151 0.273 0.019 0.780 0.533 0.455 0.633 0.5549

0.011 0.241 0.022 0.258 0.105 0.286 0.050 0.720 0.865 0.509 0.585 0.5899

0.014 0.256 0.027 0.227 0.119 0.241 0.009 0.811 0.955 0.491 0.453 0.5549

0.029 0.276 0.051 0.302 0.151 0.273 0.001 0.871 0.865 0.509 1.000 0.4559

0.018 0.727 0.015 0.767 0.318 0.636 0.177 0.356 0.396 0.600 0.219 0.35711

0.027 0.688 0.027 0.742 0.289 0.636 0.107 0.295 0.282 0.655 0.539 0.42911

0.007 0.256 0.006 0.202 0.236 0.318 0.061 0.720 0.126 0.291 0.838 0.52713

0.021 0.281 0.036 0.283 0.151 0.318 0.041 0.689 0.955 0.509 0.682 0.42914

0.004 0.256 0.043 0.252 0.005 0.105 0.695 0.495 0.047 0.818 0.375 0.59815

0.005 0.227 0.020 0.240 0.039 0.195 0.128 0.677 0.955 0.509 0.891 0.49117

0.009 0.276 0.005 0.202 0.349 0.364 0.026 0.737 0.047 0.236 0.495 0.42917

0.007 0.227 0.009 0.215 0.151 0.332 0.050 0.737 0.336 0.345 0.585 0.49117

0.003 0.767 0.002 0.816 0.170 0.727 0.896 0.538 0.126 0.727 0.076 0.28621

0.086 0.668 0.194 0.643 0.134 0.727 0.862 0.508 0.336 0.345 0.034 0.2502

0.005 0.747 0.007 0.791 0.119 0.682 0.572 0.386 0.610 0.564 0.633 0.5892

0.033 0.301 0.067 0.333 0.134 0.286 0.010 0.798 0.955 0.509 0.339 0.3932

0.033 0.321 0.022 0.283 0.454 0.409 0.013 0.768 0.193 0.291 0.076 0.2504

0.071 0.688 0.181 0.643 0.105 0.773 0.602 0.586 0.396 0.345 0.024 0.2145

0.005 0.767 0.022 0.717 0.029 0.818 0.794 0.447 0.462 0.400 0.024 0.2145

indicates data missing or illegible when filed

TABLE L Biomarkers identified in F6CSH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 P P P P P P M/Z

value ROC value ROC value ROC value ROC value ROC value ROC D

0.006 0.251 0.067 0.314 0.006 0.167 0.024 0.768 0.546 0.636 0.682 0.429

0.003 0.233 0.036 0.264 0.005 0.129 0.021 0.768 0.615 0.591 0.585 0.429

0.004 0.214 0.043 0.264 0.005 0.129 0.017 0.798 0.615 0.591 0.495 0.4291

0.016 0.270 0.136 0.314 0.010 0.167 0.012 0.828 0.421 0.636 0.413 0.3931

0.022 0.285 0.169 0.357 0.012 0.197 0.012 0.798 0.366 0.636 0.375 0.3571

0.036 0.303 0.252 0.407 0.014 0.197 0.017 0.798 0.366 0.674 0.495 0.3931

0.095 0.337 0.423 0.426 0.033 0.197 0.024 0.768 0.315 0.674 0.633 0.4201

0.174 0.337 0.620 0.475 0.050 0.235 0.050 0.720 0.315 0.674 0.633 0.4201

0.202 0.356 0.703 0.475 0.050 0.235 0.050 0.720 0.269 0.674 0.633 0.4201

0.157 0.374 0.593 0.475 0.044 0.197 0.056 0.720 0.421 0.636 0.946 0.4911

0.019 0.322 0.136 0.357 0.014 0.167 0.021 0.768 0.315 0.629 0.633 0.4201

0.126 0.666 0.117 0.667 0.467 0.606 0.338 0.417 0.108 0.773 0.785 0.5271

0.066 0.666 0.032 0.692 0.614 0.568 0.459 0.386 0.108 0.720 0.219 0.3481

0.002 0.778 0.002 0.791 0.083 0.720 0.061 0.265 0.688 0.538 0.682 0.4911

0.008 0.759 0.004 0.791 0.314 0.682 0.384 0.417 0.191 0.667 0.152 0.3211

0.006 0.782 0.004 0.841 0.198 0.682 0.514 0.417 0.688 0.530 0.172 0.3211

0.089 0.651 0.039 0.717 0.737 0.568 0.794 0.477 0.132 0.720 0.029 0.2501

0.001 0.816 0.001 0.841 0.083 0.720 0.068 0.265 0.228 0.720 0.076 0.2861

0.000 0.834 0.001 0.841 0.050 0.720 0.015 0.235 0.421 0.583 0.733 0.4551

0.002 0.759 0.003 0.791 0.104 0.720 0.030 0.235 0.366 0.583 0.633 0.4911

0.023 0.741 0.022 0.767 0.263 0.644 0.139 0.356 0.366 0.629 0.133 0.3211

0.008 0.741 0.008 0.791 0.162 0.682 0.045 0.295 0.688 0.530 0.306 0.3931

0.012 0.741 0.008 0.767 0.287 0.644 0.258 0.356 0.366 0.621 0.246 0.3571

0.001 0.797 0.001 0.816 0.083 0.758 0.074 0.326 0.482 0.538 0.172 0.3571

0.044 0.684 0.147 0.643 0.065 0.720 0.037 0.265 0.269 0.364 0.076 0.7321

0.017 0.277 0.013 0.246 0.287 0.379 0.663 0.568 0.132 0.273 0.275 0.6701

0.003 0.247 0.003 0.209 0.117 0.273 0.151 0.659 0.228 0.318 0.891 0.5271

0.027 0.318 0.039 0.326 0.179 0.273 0.107 0.689 0.482 0.409 0.785 0.4551

0.031 0.314 0.032 0.252 0.263 0.341 0.207 0.629 0.315 0.318 0.785 0.5541

0.023 0.285 0.051 0.283 0.104 0.273 0.572 0.598 0.688 0.629 0.306 0.5981

0.016 0.281 0.036 0.258 0.093 0.273 0.317 0.629 0.841 0.545 0.682 0.5271

0.015 0.285 0.007 0.209 0.401 0.348 0.258 0.629 0.088 0.273 1.000 0.5001

0.044 0.299 0.016 0.233 0.654 0.417 0.296 0.616 0.044 0.182 0.682 0.5631

0.054 0.333 0.047 0.295 0.370 0.417 0.361 0.598 0.546 0.402 0.495 0.5981

0.036 0.295 0.020 0.227 0.467 0.417 0.223 0.629 0.191 0.318 0.946 0.5271

0.001 0.816 0.001 0.816 0.038 0.758 0.050 0.265 0.421 0.629 0.539 0.598

0.000 0.816 0.002 0.841 0.019 0.795 0.117 0.295 0.688 0.500 0.785 0.536

0.002 0.778 0.047 0.717 0.002 0.909 0.098 0.326 0.108 0.273 0.585 0.607

0.036 0.703 0.268 0.618 0.012 0.833 0.090 0.326 0.088 0.273 0.891 0.500

0.058 0.670 0.321 0.618 0.022 0.795 0.338 0.417 0.228 0.318 0.891 0.455

0.041 0.684 0.101 0.667 0.104 0.720 0.164 0.295 1.000 0.500 0.275 0.661

0.044 0.666 0.109 0.692 0.104 0.720 0.068 0.295 0.688 0.455 0.339 0.661

0.039 0.303 0.051 0.283 0.218 0.341 0.056 0.720 0.482 0.409 0.633 0.429

0.004 0.759 0.009 0.742 0.050 0.758 0.019 0.205 0.920 0.485 0.375 0.357

0.006 0.741 0.009 0.767 0.093 0.720 0.240 0.356 0.315 0.674 0.172 0.625

0.000 0.853 0.000 0.866 0.009 0.871 0.240 0.326 0.421 0.629 0.275 0.348

0.000 0.890 0.000 0.866 0.007 0.909 0.296 0.374 0.228 0.682 0.246 0.348

0.005 0.741 0.012 0.742 0.057 0.720 0.361 0.386 0.688 0.492 0.785 0.420

0.041 0.684 0.268 0.568 0.016 0.795 0.602 0.465 0.108 0.227 0.453 0.429

0.977 0.483 0.169 0.376 0.050 0.758 0.408 0.417 0.021 0.182 0.733 0.527

0.282 0.572 0.939 0.494 0.044 0.720 0.037 0.295 0.005 0.091 0.020 0.777

0.062 0.684 0.340 0.618 0.022 0.795 0.128 0.326 0.044 0.182 0.024 0.768

0.015 0.707 0.032 0.717 0.093 0.727 0.207 0.326 0.315 0.311 0.152 0.670

0.044 0.684 0.039 0.717 0.341 0.644 0.277 0.356 0.763 0.583 0.785 0.5368

0.008 0.759 0.008 0.791 0.162 0.644 0.296 0.417 0.615 0.583 0.585 0.5369

0.002 0.229 0.002 0.159 0.117 0.273 0.572 0.538 0.191 0.273 0.946 0.49110

0.008 0.251 0.008 0.215 0.198 0.348 0.602 0.586 0.366 0.364 0.219 0.32110

0.070 0.647 0.086 0.667 0.287 0.644 0.019 0.235 1.000 0.538 0.219 0.62510

0.034 0.684 0.032 0.742 0.287 0.606 0.223 0.356 0.482 0.583 0.133 0.67010

0.282 0.618 0.593 0.574 0.179 0.689 0.240 0.629 0.615 0.409 0.152 0.32112

0.001 0.154 0.001 0.134 0.065 0.273 0.192 0.598 0.108 0.273 0.633 0.59813

0.001 0.195 0.007 0.215 0.019 0.235 0.728 0.556 0.615 0.409 0.785 0.53616

0.269 0.393 0.760 0.500 0.083 0.273 0.408 0.568 0.159 0.720 0.041 0.21418

indicates data missing or illegible when filed

TABLE M Biomarkers identified in F6ISL Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 M/

P P P P P P (avg

value ROC value ROC value ROC value ROC value ROC value ROC D

0.427 0.580 0.500 0.553 0.568 0.542 0.840 0.549 0.920 0.492 0.050 0.281

0.012 0.717 0.003 0.792 0.568 0.583 0.518 0.569 0.132 0.712 0.245 0.344

0.068 0.647 0.095 0.686 0.254 0.667 0.353 0.403 0.688 0.583 0.178 0.313

0.427 0.462 0.619 0.473 0.407 0.375 0.024 0.757 0.841 0.500 0.159 0.313

0.020 0.717 0.051 0.712 0.087 0.708 0.196 0.326 0.228 0.311 0.759 0.531

0.691 0.521 0.227 0.606 0.325 0.333 0.052 0.736 0.191 0.712 0.426 0.406

0.771 0.501 0.722 0.473 0.276 0.625 0.903 0.438 0.228 0.311 0.245 0.375

0.053 0.678 0.102 0.659 0.162 0.667 0.419 0.354 0.920 0.500 0.358 0.375

0.214 0.384 0.241 0.394 0.468 0.417 0.115 0.687 0.763 0.492 0.426 0.406

0.255 0.392 0.644 0.473 0.120 0.292 0.293 0.410 0.421 0.591 0.178 0.375

0.037 0.333 0.088 0.314 0.108 0.292 0.225 0.375 0.920 0.538 1.000 0.500

0.057 0.333 0.060 0.288 0.325 0.375 0.492 0.569 0.841 0.447 0.951 0.531

0.177 0.412 0.005 0.182 0.147 0.667 0.210 0.625 0.007 0.091 0.327 0.406

0.937 0.501 0.831 0.527 0.876 0.458 0.353 0.403 0.920 0.583 0.426 0.375

0.032 0.294 0.095 0.314 0.078 0.292 0.182 0.667 0.688 0.583 0.426 0.406

0.112 0.638 0.522 0.606 0.029 0.792 0.657 0.451 0.159 0.273 0.426 0.375

0.427 0.588 0.145 0.659 0.568 0.458 0.157 0.653 0.366 0.629 0.806 0.469

0.007 0.717 0.006 0.792 0.213 0.667 0.196 0.326 0.191 0.720 0.806 0.469

0.024 0.697 0.394 0.580 0.002 0.917 0.057 0.299 0.012 0.136 0.126 0.688

0.383 0.580 0.522 0.606 0.437 0.583 0.097 0.354 0.763 0.455 0.298 0.625

0.002 0.776 0.001 0.845 0.195 0.667 0.840 0.465 0.269 0.667 1.000 0.500

0.000 0.815 0.002 0.818 0.010 0.875 0.903 0.493 0.269 0.356 0.327 0.625

0.014 0.737 0.030 0.739 0.097 0.708 0.135 0.326 0.920 0.500 0.426 0.406

0.021 0.697 0.009 0.765 0.468 0.625 0.135 0.354 0.070 0.765 0.854 0.438

0.057 0.678 0.028 0.765 0.604 0.583 0.353 0.438 0.108 0.720 0.426 0.625

0.015 0.717 0.004 0.792 0.604 0.583 0.097 0.326 0.315 0.667 0.501 0.563

0.008 0.737 0.001 0.818 0.641 0.542 0.069 0.243 0.108 0.758 0.759 0.531

0.011 0.737 0.001 0.845 0.756 0.542 0.135 0.326 0.108 0.758 0.903 0.469

0.001 0.795 0.000 0.845 0.133 0.750 0.005 0.188 0.012 0.856 0.501 0.4064

0.010 0.717 0.002 0.792 0.604 0.583 0.057 0.326 0.027 0.848 0.142 0.3134

0.034 0.678 0.065 0.712 0.147 0.708 0.241 0.382 1.000 0.492 0.245 0.3754

0.002 0.776 0.011 0.765 0.020 0.792 0.716 0.438 0.763 0.500 0.806 0.5004

0.024 0.678 0.017 0.712 0.351 0.625 0.069 0.299 0.159 0.712 0.540 0.5944

0.177 0.638 0.189 0.659 0.468 0.583 0.115 0.326 0.546 0.629 0.462 0.5634

0.244 0.619 0.500 0.580 0.195 0.667 0.467 0.403 0.482 0.402 0.668 0.5944

0.383 0.423 0.166 0.341 0.756 0.583 0.332 0.604 0.366 0.356 0.111 0.3134

0.014 0.294 0.006 0.235 0.407 0.375 0.275 0.618 0.191 0.318 0.270 0.3446

0.005 0.255 0.003 0.208 0.276 0.375 0.052 0.736 0.159 0.318 0.058 0.2506

0.003 0.255 0.002 0.182 0.254 0.375 0.353 0.625 0.108 0.273 0.358 0.3758

0.001 0.216 0.000 0.129 0.276 0.333 0.010 0.785 0.035 0.227 0.806 0.4698

0.015 0.275 0.006 0.208 0.468 0.417 0.075 0.701 0.088 0.227 0.462 0.4068

0.010 0.266 0.012 0.235 0.178 0.292 0.115 0.681 0.482 0.409 0.951 0.4389

0.042 0.305 0.030 0.261 0.407 0.375 0.135 0.653 0.191 0.318 0.903 0.5009

0.034 0.344 0.043 0.288 0.233 0.333 0.182 0.625 0.421 0.364 0.806 0.50013

0.020 0.305 0.016 0.235 0.300 0.375 0.063 0.681 0.366 0.364 0.540 0.40617

0.028 0.286 0.055 0.288 0.133 0.292 0.157 0.632 0.763 0.538 0.159 0.34428

0.026 0.294 0.051 0.288 0.133 0.333 0.146 0.681 0.920 0.492 0.086 0.28134

0.112 0.638 0.060 0.712 0.717 0.542 0.419 0.410 0.688 0.576 0.327 0.37551

0.026 0.294 0.023 0.261 0.300 0.375 0.210 0.653 0.688 0.409 0.358 0.37566

0.016 0.294 0.017 0.261 0.213 0.333 0.069 0.708 0.615 0.409 0.159 0.31368

indicates data missing or illegible when filed

TABLE N Biomarkers identified in F6ISH Babesia Babesia vs. HealthyBabesia Babesia vs. Babesia vs. Babesia 1 vs. Non- vs. Flu-likewith/Lyme Flu-like vs. babesia Healthy symptoms Disease symptoms Babesia2 P P P P P P M/Z

value ROC value ROC value ROC value ROC value ROC value ROC D

0.019 0.267 0.007 0.164 0.551 0.411 0.150 0.651 0.126 0.291 0.063 0.247

0.021 0.267 0.008 0.189 0.551 0.411 0.217 0.682 0.234 0.291 0.033 0.208

0.032 0.288 0.022 0.237 0.412 0.411 0.304 0.651 0.193 0.291 0.042 0.208

0.025 0.288 0.012 0.215 0.502 0.411 0.111 0.735 0.193 0.291 0.052 0.208

0.030 0.309 0.013 0.237 0.551 0.411 0.181 0.651 0.157 0.291 0.077 0.247

0.035 0.306 0.025 0.237 0.412 0.411 0.198 0.651 0.193 0.291 0.077 0.247

0.025 0.288 0.015 0.237 0.412 0.411 0.150 0.682 0.234 0.291 0.042 0.208

0.032 0.288 0.022 0.237 0.412 0.411 0.150 0.704 0.282 0.345 0.042 0.208

0.045 0.309 0.031 0.263 0.456 0.411 0.111 0.651 0.234 0.345 0.042 0.208

0.042 0.309 0.017 0.240 0.655 0.456 0.136 0.713 0.079 0.236 0.189 0.325

0.062 0.330 0.022 0.240 0.823 0.456 0.304 0.620 0.100 0.291 0.390 0.403

0.062 0.330 0.017 0.265 0.941 0.456 0.411 0.590 0.079 0.236 0.497 0.364

0.078 0.351 0.025 0.265 0.941 0.456 0.304 0.620 0.079 0.236 0.390 0.364

0.053 0.330 0.015 0.240 0.881 0.494 0.217 0.651 0.079 0.236 0.221 0.364

0.010 0.267 0.003 0.189 0.456 0.367 0.217 0.651 0.100 0.291 0.441 0.403

0.006 0.267 0.002 0.189 0.371 0.367 0.045 0.744 0.062 0.236 0.160 0.325

0.027 0.306 0.019 0.263 0.371 0.322 0.072 0.691 0.157 0.291 0.113 0.2861

0.098 0.347 0.048 0.288 0.766 0.450 0.165 0.651 0.126 0.236 0.160 0.2471

0.084 0.326 0.039 0.263 0.766 0.450 0.165 0.682 0.079 0.236 0.556 0.4421

0.078 0.344 0.031 0.285 0.823 0.450 0.123 0.682 0.079 0.236 0.497 0.4031

0.053 0.330 0.031 0.265 0.551 0.411 0.051 0.704 0.234 0.291 0.063 0.2471

0.105 0.368 0.072 0.313 0.602 0.411 0.123 0.682 0.396 0.400 0.052 0.2471

0.035 0.694 0.043 0.687 0.233 0.672 0.136 0.352 0.777 0.545 0.751 0.5581

0.027 0.715 0.028 0.763 0.264 0.672 0.280 0.383 0.955 0.491 0.684 0.5191

0.049 0.694 0.096 0.687 0.136 0.672 0.719 0.475 0.777 0.509 0.751 0.4811

0.027 0.715 0.031 0.737 0.233 0.717 0.237 0.352 0.396 0.673 0.390 0.6361

0.045 0.715 0.053 0.687 0.264 0.672 0.537 0.475 0.777 0.564 0.684 0.5191

0.030 0.736 0.048 0.737 0.157 0.717 0.123 0.321 0.777 0.455 0.160 0.7141

0.007 0.736 0.022 0.712 0.044 0.761 0.607 0.444 0.955 0.455 0.221 0.7141

0.049 0.694 0.096 0.687 0.136 0.717 0.440 0.414 0.692 0.400 0.221 0.6751

0.038 0.694 0.150 0.636 0.037 0.806 0.959 0.506 0.396 0.400 0.342 0.5971

0.112 0.674 0.208 0.662 0.180 0.717 0.877 0.506 0.533 0.455 0.751 0.5581

0.042 0.694 0.072 0.712 0.157 0.717 0.607 0.475 0.865 0.491 0.189 0.7141

0.129 0.656 0.445 0.588 0.044 0.761 0.918 0.537 0.157 0.291 0.821 0.4811

0.049 0.698 0.065 0.715 0.233 0.628 0.537 0.383 0.692 0.564 0.298 0.6361

0.190 0.635 0.138 0.664 0.709 0.589 0.382 0.620 0.336 0.655 0.342 0.6361

0.012 0.740 0.005 0.813 0.412 0.589 0.918 0.497 0.396 0.655 0.821 0.5191

0.004 0.802 0.009 0.788 0.062 0.767 0.719 0.435 0.533 0.436 0.751 0.4421

0.013 0.740 0.004 0.813 0.551 0.589 0.918 0.528 0.396 0.600 0.964 0.5191

0.058 0.677 0.017 0.763 0.881 0.500 0.758 0.466 0.157 0.709 0.258 0.6361

0.049 0.698 0.048 0.737 0.333 0.589 0.797 0.537 0.777 0.600 0.497 0.5971

0.038 0.719 0.019 0.788 0.551 0.583 0.643 0.568 0.157 0.709 0.298 0.3641

0.073 0.656 0.017 0.763 0.941 0.494 0.237 0.682 0.100 0.764 0.821 0.5191

0.535 0.552 0.445 0.588 0.941 0.539 0.382 0.599 0.533 0.618 0.618 0.4031

0.679 0.531 0.393 0.588 0.602 0.450 0.198 0.630 0.336 0.618 0.342 0.6361

0.030 0.715 0.116 0.662 0.037 0.806 0.607 0.383 0.234 0.291 0.113 0.7531

0.730 0.549 0.787 0.535 0.766 0.539 0.382 0.599 0.865 0.455 0.094 0.714

0.704 0.451 0.472 0.588 0.044 0.233 0.100 0.321 0.020 0.873 0.821 0.519

0.227 0.368 0.787 0.462 0.031 0.189 0.440 0.444 0.011 0.927 0.751 0.558

0.255 0.410 0.787 0.462 0.044 0.189 0.959 0.528 0.015 0.927 0.342 0.325

0.017 0.271 0.096 0.316 0.017 0.194 0.797 0.506 0.157 0.727 0.189 0.364

0.007 0.250 0.031 0.268 0.025 0.194 0.217 0.651 0.777 0.564 0.077 0.247

0.005 0.226 0.006 0.215 0.118 0.278 0.111 0.682 0.396 0.400 0.033 0.208

0.011 0.247 0.017 0.240 0.118 0.278 0.123 0.642 0.610 0.455 0.094 0.286

0.004 0.799 0.008 0.813 0.074 0.761 0.681 0.444 0.777 0.545 0.684 0.519

0.000 0.861 0.001 0.864 0.021 0.850 0.471 0.414 0.396 0.673 0.892 0.519

0.084 0.635 0.243 0.639 0.074 0.717 0.382 0.599 0.234 0.345 0.160 0.714

0.010 0.757 0.025 0.763 0.062 0.806 0.837 0.444 0.777 0.455 0.002 0.909

0.023 0.736 0.015 0.737 0.371 0.628 0.382 0.404 0.157 0.709 0.892 0.519

0.019 0.740 0.003 0.838 0.881 0.500 0.304 0.620 0.011 0.873 0.892 0.481

0.005 0.760 0.015 0.740 0.037 0.806 0.837 0.537 0.692 0.564 0.892 0.481

0.067 0.677 0.138 0.662 0.136 0.722 0.150 0.651 0.955 0.491 0.342 0.597

0.138 0.639 0.345 0.591 0.101 0.717 0.040 0.765 0.234 0.291 0.160 0.714

0.010 0.778 0.053 0.712 0.017 0.850 0.643 0.568 0.462 0.345 0.160 0.714

0.013 0.247 0.010 0.240 0.264 0.361 0.035 0.744 0.282 0.345 0.964 0.4811

0.014 0.267 0.022 0.265 0.136 0.283 0.150 0.651 0.865 0.455 0.221 0.3251

indicates data missing or illegible when filed

TABLE O Fraction Protein Chip and Marker M/Z (kDa) P-Value BeamIntensity M7.06 0.028366 F1CSL M7.19 0.012611 F1CSL M7.16 0.001 F1CSLM7.48 0.005159 F1CSL M7.66 0.049366 F1CSL M8.93 0.049366 F1CSL M15.560.003012 F1CSL M44.23 0.000507 F1CSL M43.63 0.006502 F1CSH M146.620.005159 F1CSH M28.05 0.058782 F3CSL M44.43 0.049366 F3CSH M51.000.00236 F4ISH M66.27 0.02345 F4ISH M78.52 0.003219 F4ISH M99.25 0.01235F4ISH M110.23 0.001856 F4ISH M131.82 0.017894 F4ISH M196.36 0.026897F4ISH M14.37 0.028366 F6CSL M33.20 0.04125 F6CSL M45.08 0.005159 F6CSLM10.23 0.023342 F1ISL M10.27 0.015564 F1ISL M13.56 0.015564 F1ISL M43.210.034294 F1ISL M44.17 0.015564 F1ISL M44.42 0.01911 F1ISL M13.860.001499 F4ISL M22.22 0.006502 F4ISL M33.27 0.001499 F4ISL M44.250.003197 F4ISL

TABLE P Identification of Certain Exemplary Biomarkers Molecular Weightm/z Avg (kDa) Fraction Protein Identity 11.1 F5ISH Ig Kappa Chain Cregion (allotype Inv(1, 2) - human (fragment) - (SEQ ID NO: 12) 12.7F1CSH Ig heavy chain V region (anti-NDA, II-1) - human (fragment) (SEQID NO: 14) 15.9 F1ISL Hemoglobin, beta (SEQ ID NO: 21) 16.5 F1ISH ChainB, Crystal structure of Human Hemogobin (SEQ ID NO: 19) 16.7 F1ISHImmunoglobulin alpha heavy chain variable region (SEQ ID NO: 10) 23.6F6CSH Alpha-1-acid glycoprotein 1 precursor (HS) (SEQ ID NO: 22) 26.5F1CSH Lipoprotein Gln I (SEQ ID NO: 11) 33.1 F6CSH Ig gamma-3 heavychain disease proteins - human(SEQ ID NO: 15) 34 F5ISL Haptoglobin[contains haptoglobin alpha chain; haptoglobin beta chain] (SEQ ID NO:6) 36.1 F5ISL Chain A, cleaved Alpha-1-Antitrypsin Polymer (SEQ ID NO:2) 39.8 F1CSL Haptoglobin precursor [contains haptoglobin alpha chain;haptoglobin beta chain] (SEQ ID NO: 7) 39.9 F1CSH Preprohaptoglobin (SEQID NO: 5) 41.3 F1ISH Haptoglobin Hp2 (SEQ ID NO: 13) 43.5 F5ISH Chain A,Modified Alpha1-Antitrypsin (Modified Alpha1- Proteinase Inhibitor)(Tetragonal Form 1) (SEQ ID NO: 3) 51.5 F1CSH Kallistatin (SEQ ID NO:18) 51.9 F5ISH Alpha-1-antitrypsin precursor (SEQ ID NO: 1) 52.5 F5ISHAlpha-1-antitrypsin precursor 52.7 F6ISH Antithrombin III (SEQ ID NO: 8)53 F6CSL vitronectin precursor (serum spreading factor)(Sprotein)(Glycoprotein 66) (SEQ ID NO: 9) 79.2 F1ISH Transferrin (SEQ IDNO: 17) 79.5 F5CSL Fibronectin 99.6 F5CSL Ceruloplasmin (SEQ ID NO: 20)167.8 F6CSH Apolipoprotein B-100 precursor (SEQ ID NO: 16) 168 F6CSHComplement C3 precursor (contains: complement C3 beta chain, complementC3 alpha chain, C3a anaphyl) (SEQ ID NO: 4)

1-88. (canceled)
 89. A method for qualifying babesia status in a subjectcomprising: measuring at least one biomarker in a biological sample fromthe subject, wherein the at least one biomarker is selected from thegroup consisting of the biomarkers of Table 1, Table 2, and Table 3; andcorrelating the measurement -with babesia status.
 90. A method fordetermining the course of babesia comprising: measuring, at a firsttime, at least one biomarker in a biological sample from the subject,wherein the at least one biomarker is selected from the group consistingof the biomarkers of Table 1, Table 2, and Table 3; measuring, at asecond time, the at least one biomarker in a biological sample from thesubject; and comparing the first measurement and the second measurement;wherein the comparative measurements determine the course of babesia.91. A method comprising measuring at least one biomarker in a samplefrom a subject, wherein the at least one biomarker is selected from thegroup consisting of biomarkers of Table 1, Table 2, and Table
 3. 92. Akit comprising: a solid support comprising at least one capture reagentattached thereto, wherein the capture reagent binds at least onebiomarker from a first group consisting of the biomarkers of Table 1,Table 2 and Table
 3. 93. The kit of claim 92, wherein the solid supportcomprising a capture reagent is a SELDI probe.
 94. The kit of claims 92or 93, additionally comprising: a container containing at least one ofthe biomarkers of Table 1, Table 2, or Table
 3. 95. The method of anyone of claims 89 to 91 or the kits of claim 92 or 94, wherein the atleast one biomarker is selected from the group consisting of biomarkersof molecular masses of about 2.8, 2.9, 3, 3.1, 3.2, 3.6, 3.8, 4, 4.1,4.2, 4.3, 4.8, 4.9, 6.4, 7, 7.1, 7.2, 7.3, 7.5, 7.7, 7.9, 8.7, 8.8, 8.9,10, 10.1, 10.2, 10.3, 10.4, 10.9, 11, 11.2, 11.3, 11.6, 11.8, 11.9,12.6, 12.7, 12.8, 12.9, 13, 13.1, 13.2, 13.6, 13.8, 14.1, 14.4, 14.7,15.1, 15.6, 15.9, 16.5, 16.7, 17.3, 17.8, 21.9, 22, 22.2, 22.3, 23.5,23.6, 25.5, 25.8, 28, 28.1, 28.2, 33, 33.1, 33.2, 33.3, 34.1, 36.1,39.8, 43.4, 44, 44.2, 44.3, 44.8, 45.1, 46.1, 47.7, 51, 53, 53.6, 60.6,62.4, 66.9, 79, 18.1, 19.2, 22.3, 26.5, 39.6, 39.9, 40.1, 41.3, 43.2,43.6, 44.2, 44.4, 44.6, 45.2, 44.7, 50, 50.5, 51.2, 51.5, 51.9, 52.5,52.7, 58.9, 59.1, 59.6, 59.8, 60.5, 61.6, 61.9, 62.3, 62.8, 64, 66.3,66.6, 78.5, 79, 79.2, 79.5, 79.6, 99.3, 99.6, 110.2, 131.8, 133.5,134.6, 146, 146.6, 167.8, 168, and 196.4 kDa.
 96. The method of any ofclaims 89, 90, or 95, further comprising: managing subject treatmentbased on the status.
 97. The method of claim 96, further comprising:measuring the at least one biomarker after subject management andcorrelating the measurement with disease progression.
 98. A compositioncomprising a purified biomolecule selected from the group consisting ofthe biomarkers of Table 1, Table 2, and Table
 3. 99. A compositioncomprising a biospecific capture reagent that specifically binds abiomolecule selected from group consisting of the biomarkers of Table 1,Table 2, and Table
 3. 100. A composition comprising a biospecificcapture reagent bound to a biomarker of Table 1, Table 2, and Table 3.101. A software product comprising: a) code that accesses dataattributed to a sample, the data comprising measurement of at least onebiomarker in the sample, the biomarker selected from the groupconsisting of the biomarkers of Table 1, Table 2, and Table 3; and b)code that executes a classification algorithm that classifies the<disease> status of the sample as a function of the measurement.
 102. Amethod comprising detecting a biomarker of Table 1, Table 2, or Table 3by mass spectrometry or immunoassay.
 103. A method for identifying acompound that interacts with a biomarker of Table 1, Table 2 or Table 3wherein said method comprises: a) contacting a biomarker of Table 1,Table 2, or Table 3 with a test compound; and b) determining whether thetest compound interacts with a biomarker of Table 1, Table 2, or Table3.